Tech Guides

The question several Deep Learning engineers may ask themselves is: Which is better, TensorFlow or CNTK? Well, we're going to answer that question for you, taking you through a closely fought match between the two most exciting frameworks. So, here we are, ladies and gentlemen, it's fight night and it's a full house. In the Red corner, weighing in at two hundred and seventy pounds of Python and topping out at over ten thousand frames per second; managed by the American tech giant, Google; we have the mighty, the beefy, TensorFlow! In the Blue corner, weighing in at two hundred and thirty pounds of C++ muscle, we have, one of the top toolkits that can comfortably scale beyond a single machine. Managed by none other than Microsoft, it's fast, it's furious, it's CNTK aka the Microsoft Cognitive Toolkit! And we're into Round One… TensorFlow and CNTK are looking quite menacingly at each other and are raging to take down their opponents. TensorFlow seems pleased that its compile times are considerably faster than its successor, Theano. Although, it looks like happiness came a tad bit soon. CNTK, light and bouncy on it's feet, comes straight out of nowhere with a whopping seventy thousand frames/second upper cut, knocking TensorFlow to the floor. TensorFlow looks like it's in no mood to give up anytime soon. It makes itself so simple to use and understand that even students can pick it up and start training their own models. This isn't the case with CNTK, as it begs to shed its complexity. On the other hand, CNTK seems to be thrashing TensorFlow in terms of 3D convolution, where CNTK can clearly recognize images from streaming content. TensorFlow also tries its best to run LSTM RNNs, but in vain. The crowd keeps cheering on… Wait a minute...are they calling out for TensorFlow? Yes they are! There's hardly any cheering for CNTK. This is embarrassing! Looks like its community support can't match up to TensorFlow's. And ladies and gentlemen, that does make a difference - we can see TensorFlow improving on several fronts and gradually getting back in the game! TensorFlow huffs and puffs as it tries to prove that it's not just about deep learning and that it has tools in the pocket that can support other algorithms such as reinforcement learning. It conveniently whips out the TensorBoard, and drops CNTK to the floor with its beautiful visualizations. TensorFlow now has the upper hand and is trying hard to pin CNTK to the floor and tries to use its R support to finish it off. But CNTK tactfully breaks loose and leaves TensorFlow on the floor - still not ready to be used in production. And there goes the bell for Round One! Both fighters look exhausted but you can see a faint twinkle in TensorFlow's eye, primarily because it survived Round One. Google seems to be working hard to prep it for Round Two and is making several improvements in terms of speed, flexibility and majorly making it ready for production. Meanwhile, Microsoft boosts CNTK's spirits with a shot of Python APIs in its blood. As it moves towards reaching version 2.0, there are a lot of improvements to CNTK, wherein, Microsoft has ensured that it's not left behind, like having a backend for Keras, which puts it on par with TensorFlow. Moreover, there seem to be quite a few experimental features that it looks ready to enter the ring with, like the Java API for example. It's the final round and boy, are these two into a serious stare-down! The referee waves them in and off they are. CNTK needs to get back at TensorFlow. Comfortably supporting multiple GPUs and CPUs out of the box, across both the Microsoft and Linux platforms, it has an advantage over TensorFlow. Is it going to use that trump card? Yes it is! A thousand GPUs and a hundred machines in, and CNTK is raining blows on TensorFlow. TensorFlow clearly drops the ball when it comes to multiple machines, and it rather complicates things. It's high time that TensorFlow turned the tables. Lo and behold! It shows off its mobile deep learning capabilities with TensorFlow Lite, clearly flipping CNTK flat on its back. This is revolutionary and a tremendous breakthrough for TensorFlow! CNTK, however, is clearly the people's choice when it comes to language compatibility. With support for C++, Python, C#/.NET and now Java, it's clearly winning in this area. Round Two is coming to an end, ladies and gentlemen and it's a neck to neck battle out there. We're not sure the judges are going to be able to choose a clear winner, from the looks of it. And…. there goes the bell! While the scores are being tallied, we go over to the teams and some spectators for some gossip on the what's what of deep learning. Did you know having multiple machine support is a huge advantage? It increases speed and efficiency by almost 10 times! That's something! We also got to know that TensorFlow is training hard and is picking up positives from its rival, CNTK. There are also rumors about a new kid called MXNet (read about it here), that has APIs in R, Python and even in Julia! This makes it one helluva framework in terms of flexibility and speed. In fact, AWS is already implementing it while Apple also is rumored to be using it. Clearly, something to watch out for. And finally, the judges have made their decision. Ladies and gentlemen, after two rounds of sheer entertainment, we have the results... TensorFlow CNTK Processing speed 0 1 Learning curve 1 0 Production readiness 0 1 Community support 1 0 CPU, GPU computation support 0 1 Mobile deep learning 1 0 Multiple language compatibility 0 1 It's a unanimous decision and just as we thought, CNTK is the heavyweight champion! CNTK clearly beat TensorFlow in terms of performance, because of its flexibility, speed and ability to use in production! As a Deep Learning engineer, should you be wanting to use one of these frameworks in your tasks, you should check out their features thoroughly, test them out with a test dataset and then implement them to your actual data. After all, it's the choices we make that define a win or a loss - simplicity over resource utilisation, or speed over platform, we must choose our tools wisely. For more information on the kind of tests that both the tools have been put through, read the Research Paper presented by Shaohuai Shi, Qiang Wang, Pengfei Xu and Xiaowen Chu from the Department of Computer Science, Hong Kong Baptist University and these benchmarks.

As one of the most widely discussed phenomena across the global media, Blockchain has certainly grown from just a hype to becoming a mainstream reality. Leading industry experts from finance, supply chain, and IoT are collaborating to make Blockchain available for commercial adoption. But while Blockchain is being projected as the future of digital transactions, it still suffers from two major limitations: carrying out private transactions and scalability. As such, a pressing need to develop a Blockchain-based distributed ledger to overcome these problems was widely felt. Enter Hyperledger Founded by Linux in 2015, Hyperledger aims at providing enterprises a platform to build robust blockchain applications for their businesses and to create open-source enterprise-grade frameworks to carry out secure business transactions. It is a fulcrum, which includes leading industries and software developers working collaboratively for building blockchain frameworks that can further be used to deploy blockchain applications for industries. With leading industry experts such as IBM, Intel, Accenture, SAP, among others collaborating with the Hyperledger community, and with the recent addition of BTS, Oracle, and Patientory Foundation, the community is gaining a lot of traction. No wonder, Brian Behlendorf, Executive Director at Hyperledger, says, “Growth and interest in Hyperledger remain high in 2017”. There are a total of 8 projects: five are frameworks (Sawtooth, Fabric, Burrow, Iroha, and Indy), and the other three are tools (Composer, Cello, and Explorer) supporting those frameworks. Each framework provides a different approach in building desired blockchain applications. Hyperledger Fabric, the community’s first framework, is contributed by IBM. It hosts smart contracts using Chaincode, an interface written in Go or Java, which contains the business logic of the ledger. Hyperledger Sawtooth, developed by Intel offers a modular blockchain architecture. It consists of Proof of Elapsed Time (PoET), a consensus algorithm developed by Intel for high efficiency among distributed ledgers. Hyperledger Burrow, a joint proposal by Intel and Monax, is a permissioned smart contract machine. It executes the smart contract code following the Ethereum specification with an engine, a strong audit trail, and a consensus mechanism. Apart from these already launched frameworks, two more - namely Indy and Iroha, are still in the incubation phase. The Hyperledger community is also building supporting tools such as  Composer which is already launched in the market and Cello and Explorer which are awaiting unveiling. [box type="shadow" align="" class="" width=""]Although a plethora of Hyperledger tools and frameworks are available, in the rest of the article we take Hyperledger Fabric - one of the most popular and trending frameworks - for the purpose of demonstrating how Hyperledger is being used by businesses.[/box] Why should businesses use Hyperledger? In order to lock down a framework upon which Blockchain apps can be built, several key aspects are worth considering. Some of the most important ones among them are portability, security, reliability, interoperability, and user-friendliness. Hyperledger as a platform offers all of the above features for building cross-platform and production-ready applications for businesses. Let’s take a simple example here to see how Hyperledger works for businesses. Consider a restaurant business. A restaurant owner buys vegetables from a wholesale shop at a much lower cost than in the market. The shopkeeper creates a network wherein other buyers cannot see the cost at which vegetables are sold to a buyer. Similarly, the restaurant owner can view only his transaction with the shopkeeper. For the vegetables to reach the restaurant, they must pass through numerous stages such as transport, delivery, and so on. The restaurant owner can track the delivery of his vegetables at each stage and so can the shopkeeper. The transport and the delivery organizations, however, won’t be able to see the transaction details. This means that the shopkeeper can establish a confidential network within a private network of other stakeholders. This type of a network can be set up using Hyperledger Fabric. Let’s break down the above example into some of the reasons to consider incorporating Hyperledger for your business networks: With Hyperledger you get performance, scalability, and multiple levels of trust. You get data on a need-to-know basis - Only the parties in the network that need the data get to know about it. Backed by bigshots like Intel and IBM, Hyperledger strives to offer a strong standard for Blockchain code which in turn provides better functionality at increased speeds. Furthermore, with the recent release of Fabric v1.0, businesses can create out-of-the-box blockchain solutions on its highly elastic and extensible architecture further eased by using Hyperledger Composer. The Composer aids businesses in creating smart contracts and blockchain applications without having to know the underlying complex intricacies of the blockchain network. It is a great fit for real-world enterprise usage, built with collaborative efforts from leading industry experts. Although Ethereum is used by many businesses, some of the reasons why Hyperledger could be a better enterprise fit are: While Ethereum is a public Blockchain, Hyperledger is a private blockchain. This means enterprises within the network know who is present on the peer nodes, unlike Ethereum. Hyperledger is a permissioned network i.e., it has the ability to grant permission on who can participate in the consensus mechanism of the Blockchain network. Ethereum, on the other hand, is permissionless. Hyperledger has no built-in cryptocurrency. Ethereum, on the other hand, has a built-in cryptocurrency, called Ether. Many applications don’t need a cryptocurrency to function, and using Ethereum there can be a disadvantage. Hyperledger gives you the flexibility of choosing a programming language such as Java or Go, for preparing smart contracts. Ethereum, on the other hand, uses Solidity which is a lot less common in use. Hyperledger is highly scalable — unlike traditional Blockchain and Ethereum — with minimal performance losses. “Since Hyperledger Fabric was designed to meet key requirements for permissioned blockchains with transaction privacy and configurable policies, we’ve been able to build solutions quickly and flexibly. ” - Mohan Venkataraman, CTO, IT People Corporation. Future of Hyperledger The Hyperledger community is expanding rapidly with many industries collaborating and offering their capabilities in building cross-industry blockchain applications. Hyperledger has found adoption within business networks in varied industries such as healthcare, finance, and supply chain to build state-of-the-art blockchain applications which assure privacy and decentralized permissioned networks. It is shaping up to be a technology which can revolutionize the way businesses deal with different access control within a consortium, with an armor of enhanced security measures. With the continuous developments in these frameworks, smarter, faster, and more secure business transactions will soon be a reality. Besides, we can expect to see Hyperledger on the cloud with IBM’s plans to extend Blockchain technologies onto its cloud. Add to that the exciting prospect of blending aspects of Artificial Intelligence with Hyperledger, transactions look more advanced, tamper-proof, and secure than ever before.

Unless you have been living under a rock, you have most likely heard about Bitcoin, the world's most popular cryptocurrency that is growing by leaps and bounds. In fact, recently, Bitcoin broke the threshold of $6000 and is now priced at an all-time high. Bitcoin is not alone in this race as another cryptocurrency named Ethereum is hot on its heels. Despite being only three years old, Ethereum is quickly emerging as a popular choice especially among enterprise users. Ethereum’s YTD price growth has been more than a whopping 3000%. In terms of market cap as well Ethereum has shown a significant increase. Its overall share of the 'total cryptocurrency market' rose from 5% at the beginning of the year to 30% YTD.  In absolute terms, today it stands at around $28 Billion.  On the other hand, Bitcoin’s market cap as a percentage of the market has shrunk from 85% at the start of the year to 55%  and is valued at around $90 Billion. Bitcoin played a huge role in bringing Ethereum into existence. The co-creator and inventor of Ethereum, Vitalik Buterin, was only 19 when his father introduced him to bitcoin and by extension, to the fascinating world of cryptocurrency. In a span of 3 years, Vitalik had written several blogs on the topic and also co-founded the Bitcoin Magazine in 2011. Though Bitcoin served as an excellent tool for money transaction eliminating the need for banks, fees, or third party, its scripting language had limitations. This led to Vitalik, along with other developers, to found Ethereum - A platform that aimed to extend beyond Bitcoin’s scope and make internet decentralized. How Ethereum differs from the reigning cryptocurrency - Bitcoin Both Bitcoin and Ethereum are built on top of Blockchain technology allowing them to build a decentralized public network. However, Ethereum’s capability extends beyond being a cryptocurrency and differs from Bitcoin substantially in terms of scope and potential. Exploiting the full spectrum blockchain platform Bitcoin leverages Blockchain's distributed ledger technology to perform secured peer-to-peer cash transactions. It thus disrupted traditional financial transaction instruments such as PayPal. Meanwhile, Ethereum aims to offer much more than digital currency by helping developers build and deploy any kind of decentralized applications on top of Blockchain. The following are some Ethereum based features and applications that make it superior to bitcoin. DApps A decentralized app or DApp refers to a program running on the internet through a network but is not under the control of any single entity. A white paper on DApp highlights the four conditions that need to be satisfied to call an application a DApp: It must be completely open-source Data and records of operation must be cryptographically stored It should utilize a cryptographic token It must generate tokens The whitepaper also goes on to suggest that DApps are the future: “decentralized applications will someday surpass the world’s largest software corporations in utility, user-base, and network valuation due to their superior incentivization structure, flexibility, transparency, resiliency, and distributed nature.” Smart Contracts and EVM Another feature that Ethereum boasts over Bitcoin is a smart contract. A smart contract works like a traditional contract. You can use it to perform a task or transfer money in return for any asset or task in an efficient manner without needing interference from a middleman. Though Bitcoin is fast, secure, and saves cost it has limitations in terms of the ability to run operations. Ethereum solves this problem by allowing operations to work as a contract by converting them to pieces of code and have them supervised by a network of computers. A tool that helps Ethereum developers build and experiment with different contracts is Ethereum Virtual Machine. It acts as a testing environment to build blockchain operations and is isolated from the main network. Thus, it gives developers a perfect platform to build and test smart as well as robust contracts across different industries. DAOs One can also create Decentralized Autonomous Organizations (DAO) using Ethereum. DAO eliminates the need for human managerial involvement. The organization runs through smart contracts that convert rules, core tasks and structure of the organization to codes monitored by a fault-tolerant network. An example of DAO is Slock.it, a DAO version of Airbnb. Performance An important factor for cryptocurrency transaction is the amount of time it takes to finalize the transaction. This is called as Block Time. In terms of performance, the Bitcoin network takes 10 minutes to make a transaction whereas Ethereum is much more efficient and boasts a block time of just 14-15 seconds. Development Ethereum’s programming language Solidity is based on JavaScript. This is great for web developers who want to use their knowledge of JavaScript to build cool DApps and extend the Ethereum platform. Moreover, Ethereum is Turing complete, meaning it can compute anything that is computable provided enough resources are available. Bitcoin, on the other hand, is based on C++ which comparatively is not a popular choice among the new generation of app developers. Community and Vision One can say Bitcoin works as a DAO with no involvement of individuals in managing the cryptocurrency and is completely decentralized and owned by the community. Satoshi Nakamoto, who prefers to stay behind the curtains, is the only name that one comes across when it comes to relating an individual with Bitcoin. The community, therefore, lacks a figurehead when it comes to seeking future directions. Meanwhile, Vitalik Buterin is hugely popular amongst Ethereum enthusiasts and is very much involved in designing the future roadmap with other co-founders. Cryptocurrency Supply Similar to Bitcoin, Ethereum has Ether which works as a digital asset that fuels the network and transactions performed on the platform. Bitcoin has a fixed supply cap of around 21 million coins. It’s going to take more than 100 years to mine the last Bitcoin after which Bitcoin would behave as a deflationary cryptocurrency. Ethereum, on the other hand, has no fixed supply cap but has restricted its annual supply to 18 million Ethers. With no upper cap on the number of Ether that can be mined, Ethereum behaves as an inflationary currency and may lose value with time. However, the Ethereum community is now planning to move from proof-of-work to proof-of-stake model which should limit the number of ethers being mined and also offer benefits such as energy efficiency and security. Some real-world applications using Ethereum The Decentralized applications’ growth has been on the rise with people starting to recognize the value offered by Blockchain and decentralization such as security, immutability, tamper-proofing, and much more. While Bitcoin uses blockchain purely as a list of transactions, Ethereum manages to transfer value and information through its platform. Thus, it allows for immense possibilities when it comes to building different DApps across a wide range of industries. The financial domain is obviously where Ethereum is finding a lot of traction. Projects such as Branche - a Decentralized Consumer Micro­credit and Financial Services and Augur, a decentralized prediction market that has raised more than $ 5 million are some prominent examples. But financial applications are only the tip of the iceberg when it comes to possibilities that Ethereum offers and potential it holds when it comes disrupting industries across various sectors. Some other sectors where Ethereum is making its presence felt are: Firstblood is a decentralized eSports platform which has raised more than $5.5 million. It allows players to test their skills and bet using Ethereum while the tournaments are tracked on smart contracts and blockchain. Alice.Si a charitable trust that lets donors invest in noble causes knowing the fact that they only pay for causes where the charity makes an impact. Chainy is an Ethereum-based authentication and verification system that permanently stores records on blockchain using timestamping. Flippening is happening! If you haven’t heard of Flippening, it’s a term coined by cryptocurrency enthusiasts on Ethereum chances of beating Bitcoin to claim the number one spot to become the largest capitalized blockchain. Comparing Ethereum to Bitcoin may not be right as both serve different purposes. Bitcoin will continue to dominate cryptocurrency but as more industries adopt Ethereum to build Smart Contracts, DApps, or DAOs of their choice, its popularity is only going to grow, subsequently making Ether more valuable. Thus, the possibility of Ether displacing Bitcoin is strong. With the pace at which Ethereum is growing and the potential it holds in terms of unleashing Blockchain’s power to transform industries, it is definitely a question of when rather than if Flippening would happen!

Hey! It’s great seeing you here. I am Ed, the Engineer and today I’m going to open up my secret toolbox and share some great tools I use to build Blockchains. If you’re a Blockchain developer or a developer-to-be, you’ve come to the right place! If you are not one, maybe you should consider becoming one. “There are only 5,000 developers dedicated to writing software for cryptocurrencies, Bitcoin, and blockchain in general. And perhaps another 20,000 had dabbled with the technology, or have written front end applications that connect with the blockchain.” - William Mougayar, The Business Blockchain Decentralized apps or dapps, as they are fondly called, are serverless applications that can be run on the client-side, within a blockchain based distributed network. We’re going to learn what the best tools are to build dapps and over the next few minutes, we’ll take these tools apart one by one. For a better understanding of where they fit into our development cycle, we’ll group them up into stages - just like the buildings we build. So, shall we begin? Yes, we can!! ;) The Foundation: Platforms The first and foremost element for any structure to stand tall and strong is its foundation. The same goes for Blockchain apps. Here, in place of all the mortar and other things, we’ve got Decentralized and Public blockchains. There are several existing networks on the likes of Bitcoin, Ethereum or Hyperledger that can be used to build dapps. Ethereum and Bitcoin are both decentralized, public chains that are open source, while Hyperledger is private and also open source. Bitcoin may not be a good choice to build dapps on as it was originally designed for peer-to-peer transactions and not for building smart contracts. The Pillars of Concrete: Languages Now, once you’ve got your foundation in place, you need to start raising pillars that will act as the skeleton for your applications. How do we do this? Well, we’ve got two great languages specifically for building dapps. Solidity It’s an object-oriented language that you can use for writing smart contracts. The best part of Solidity is that you can use it across all platforms - making it the number one choice for many developers to use. It’s a lot like JavaScript and way more robust than other languages. Along with Solidity, you might want to use Solc, the compiler for Solidity. At the moment, Solidity is the language that’s getting the most support and has the best documentation. Serpent Before the dawn of Solidity, Serpent was the reigning language for building dapps. Something like how bricks replaced stone to build massive structures. Serpent though is still being used in many places to build dapps and it has great real-time garbage collection. The Transit Mixers: Frameworks After you choose your language to build dapps, you need a framework to simplify the mixing of concrete to build your pillars. I find these frameworks interesting: Embark This is a framework for Ethereum you can use to quicken development and to streamline the process by using tools or functionalities. It allows you to develop and deploy dapps easily, or even build a serverless HTML5 application that uses decentralized technology. It equips you with tools to create new smart contracts which can be made available in JavaScript code. Truffle Here is another great framework for Ethereum, which boasts of taking on the task of managing your contract artifacts for you. It includes support for the library that links complex Ethereum apps and provides custom deployments. The Contractors: Integrated Development Environments Maybe, you are not the kind that likes to build things from scratch. You just need a one-stop place where you can tell what kind of building you want and everything else just falls in place. Hire a contractor. If you’re looking for the complete package to build dapps, there are two great tools you can use, Ethereum Studio and Remix (Browser-Solidity). The IDE takes care of everything - right from emulating the live network to testing and deploying your dapps. Ethereum Studio This is an adapted version of Cloud9, built for Ethereum with some additional tools. It has a blockchain emulator called the sandbox, which is great for writing automated tests. Fair warning: You must pay for this tool as it’s not open source and you must use Azure Cloud to access it. Remix  This can pretty much do the same things that Ethereum Studio can. You can run Remix from your local computer and allow it to communicate with an Ethereum node client that’s on your local machine. This will let you execute smart contracts while connected to your local blockchain. Remix is still under development during the time of writing this article. The Rebound Hammer: Testing tools Nothing goes live until it’s tried and tested. Just like the rebound hammer you may use to check the quality of concrete, we have a great tool that helps you test dapps. Blockchain Testnet For testing purposes, use the testnet, an alternative blockchain. Whether you want to create a new dapp using Ethereum or any other chain, I recommend that you use the related testnet, which ideally works as a substitute in place of the true blockchain that you will be using for the real dapp. Testnet coins are different from actual bitcoins, and do not hold any value, allowing you as a developer or tester to experiment, without needing to use real bitcoins or having to worry about breaking the primary bitcoin chain. The Wallpaper: dapp Browsers Once you’ve developed your dapp, it needs to look pretty for the consumers to use. Dapp browsers are mostly the User Interfaces for the Decentralized Web. Two popular tools that help you bring dapps to your browser are Mist and Metamask. Mist  It is a popular browser for decentralized web apps. Just as Firefox or Chrome are for the Web 2.0, the Mist Browser will be for the decentralized Web 3.0. Ethereum developers would be able to use Mist not only to store Ether or send transactions but to also deploy smart contracts. Metamask  With Metamask, you can comfortably run dapps in your browser without having to run a full Ethereum node. It includes a secure identity vault that provides a UI to manage your identities on various sites, as well as sign blockchain contracts. There! Now you can build a Blockchain! Now you have all the tools you need to make amazing and reliable dapps. I know you’re always hungry for more - this Github repo created by Christopher Allen has a great listing of tools and resources you can use to begin/improve your Blockchain development skills. If you’re one of those lazy-but-smart folks who want to get things done at the click of a mouse button, then BaaS or Blockchain as a Service is something you might be interested in. There are several big players in this market at the moment, on the likes of IBM, Azure, SAP and AWS. BaaS is basically for organizations and enterprises that need blockchain networks that are open, trusted and ready for business. If you go the BaaS way, let me warn you - you’re probably going to miss out on all the fun of building your very own blockchain from scratch. With so many banks and financial entities beginning to set up their blockchains for recording transactions and transfer of assets, and investors betting billions on distributed ledger-related startups, there are hardly a handful of developers out there, who have the required skills. This leaves you with a strong enough reason to develop great blockchains and sharpen your skills in the area. Our Building Blockchain Projects book should help you put some of these tools to use in building reliable and robust dapps. So what are you waiting for? Go grab it now and have fun building blockchains!

What is the secret behind a great video game? Different people will give different answers to these questions. Some say that you need to have this unwavering vision of a game design and apply it to the game, but others say that you need to research the market and see what games people like to buy. However, there is one answer that everyone would agree with, and that is,to make a great game, you need to have a great team. Before we go further talking about building a great development team, let's clarify one thing first. Forming a team can happen in two ways, with the first one starting with zero team members and recruiting new people into the team. The other one is adding to people from an already-formed team to make a new, additional team. Fortunately, the process of building a great team is the same for both methods. All right, with that out of the way, let's proceed. The very first aspect to tackle when you want to build a game development team is about scale. How big is the game you want to make, and in turn, how big is the team you need to make that game? After all, making a big game with a small team is very hard, and making a small game with a big team is a waste of resources. No matter the size of game you want to make, though, it's always better to start off with a small, core team. Keep in mind that the difference in scale will affect the type of person you want on this initial core team. In a small project, it'd be fine to have people that usually work alone in the core team. But in a larger project, you want people that have leadership qualities for the core team members, because they're expected to manage their own subordinates when the team size grows. Roles are key Now that we have determined the scale of our project, let's focus on the next important aspect: roles. When we're building a game development team, it's pretty crucial to determine what roles are needed and how many people are required to fill those roles. There are a couple of things that affect team roles. One of them is the type of games you're going to make. If you're making a story-rich game with a lot of dialogues, then having a writer (or even multiple writers) on the team is pretty important. On the other hand, if you're making a racing game, which usually doesn't have a big narrative aspect, then having a programmer that can simulate car physics is much more important than having a writer. And again, scale matters. In a big development team, you’re going to want specialized roles for the team, like network programmer, engine programmer, gameplay programmer, and so on. Meanwhile, in a smaller development team, you're better off with general roles and people who can work in various fields. And if the team size is small enough, it's not strange for people to have multiple roles, like a programmer that is also a game designer. Explore outsourcing opportunities Another aspect that you should think about is outsourcing. How much of your game development should be done in-house? Which part of the game can be outsourced to another party that turns out okay? It's quite common to contract third parties to produce music and artwork for the game, while programming generally tends to be fully done in-house. Identifying this aspect is quite important to determine the actual team you're going to need to build your game. Okay, after determining the roles needed in our team, here comes the next important thing: what kind of people do we want for those roles? Sure, you're going to want the usual traits, like hardworking, honest, passionate, and all the other CV building words that all employers seek. But are there any specific traits that you want for a member of a game development team? Collaboration and ownership Game development is a massive collaboration between multiple disciplines. And in this kind of collaboration, communication is key to making a project successful. So that's the one trait that you want from the people in your team: being able to collaborate with others. For example, it's pretty great to have artists that are able communicate the visual that they want in terms that programmers can understand, and vice versa. Having great people isn't enough to build a great team, however. To spur people to make great works, they need to have a sense of ownership over the project they're working on. There are many ways to foster this sense of ownership among the team members, but in essence, the people in the team need to feel like they're valued and they have some sort of control over their work. Instead of being told how something is supposed to be done, it's better to have a discussion about how something should be done. There are still many aspects of a game development team that we haven't covered, but hopefully, all of the things we have discussed will help you in making a great team. Good luck! About the Author Raka Mahesa is a game developer at Chocoarts (http://chocoarts.com/), who is interested in digital technology in general. Outside of work hours, he likes to work on his own projects, with Corridoom VR being his latest released game. Raka also regularly tweets as @legacy99.

While it's true that a tool is only as good as its user, there's also another saying, that a good carpenter should sharpen the axe before chopping down a tree. So yes, effective tools matter, whether it's for something physical like carpentry or something digital like video game development. And that's why in this post we're going to be talking about the best graphics and rendering tools for game development. Before we continue though, let's take a moment to discuss what counts graphics and rendering tools are, exactly. For starters, game engines and frameworks are not going to be included in this list. Yes, that software is used to render stuff, but they are game creation tools, not tools specifically for graphics. General image editors and 3D editors are also not going to be included here, because those tools are meant to be general and not specifically tailored for video game development. What are the best graphics and rendering tools? So, with that out of the way, let's start listing the very best graphics and rendering tools. We will start with tools that are specific for 2D games, then tools that are for 3D games, and lastly, tools that can be used for either 2D or 3D games. Aseprite Aseprite is an image editor geared specifically for pixel art. It has various features to make creating pixel art sprite easier, like color palette editor, pixel-perfect pencil tool, frame-based animation editor, and a smart image rotation algorithm that avoids pixel distortion. And of course, it has the usual features of a modern image editor, like layer and transparency control. Spine Spine, in short, is a tool for creating 2D skeletal animation specifically for games. By using skeletal animation, artists no longer need to create animation frame-by-frame, and can simply animate the required part. So instead of making 10 full images of a character walking, an artist just needs to move the body part images to the desired position to create a walking animation. That animation can then be exported with JSON format and be used in a game engine. Enlighten Enlighten is a tool that can be integrated to a game to provide real-time, physic-based lighting. Physic-based lighting is usually not used in video games because they're slow to compute, however Enlighten manages to approximate this lighting system with a much faster calculation process. Enlighten is also the main technology behind Unity's Physic Based Rendering feature that was introduced with Unity 5. SpeedTree Foliage has always been one of the hardest things to achieve in 3D rendering. Fortunately, we have SpeedTree now, which is a tool that enables video games to render vegetation easily. SpeedTree provides a vegetation modeling tool that allows developer to quickly create 3D trees and plants, as well as an SDK that can be integrated into a game to render vegetation beautifully and efficiently. Substance Designer Substance Designer is a material authoring tool, which is a tool to process and create textures for 3D objects. Using this software, game developer can decide how an object would look in game and create the appropriate textures and configuration for the object. Umbra 3D rendering is quite a heavy task for computers, especially when a video game features a gigantic, complex environment. So, optimizing the rendering process is really important to make sure these games always run smoothly, and Umbra is a tool that can help game developers do just that. Umbra can process a 3D scene and calculate which objects are visible and which are not, making sure the GPU only renders the necessary objects in the scene. CrazyBump 3D objects usually have additional data that describes how a particular object would look when rendered. One of these additional data is normal map, a texture that describes the smoothness of an object's surface. Normal maps are important because they can make objects appear to be rough. CrazyBump allows developer to quickly generate a normal map from a texture. So if you have a rocky texture, CrazyBump can analyze the contrast of that texture and generate the appropriate normal map. Littera Many video games use a technique called bitmap font to render text on screen. This technique uses an image containing all the letters written in a font and renders letters to form a text. Littera is a tool to generate such image from a font type. With Littera developers can also customize the rendered font further by adding outlines or using gradient to color the letters. STG STG stands for Seamless Texture Generator, and, as the name implies, it's a tool that provides game developer with seamless textures that can be tiled. STG is able to process a digital photo and generate a seamless texture based on that photo. This is a really handy tool for creating realistic ground, grass, wall, and other textures that can be applied on a big surface. TexturePacker The last one on this list is TexturePacker, and being the last one certainly doesn't mean it's the least important, because this is a really useful tool. TexturePacker is a tool that can pack multiple images into a single texture, using the most efficient layout possible. This technique is called texture atlas, and it's a really great thing to have in video games, because having fewer texture files will reduce the rendering load and optimize the game. About the Author RakaMahesa is a game developer at Chocoarts (http://chocoarts.com/), who is interested in digital technology in general. Outside of work hours, he likes to work on his own projects, with Corridoom VR being his latest released game. Raka also regularly tweets as @legacy99.

The word 'botnet' is formed from the words ‘robot’ and ‘network’. Cybercriminals use special Trojan viruses to breach the security of several users’ computers, taking control of each computer and organizing all of the infected machines into a network of ‘bots’ that the criminal can remotely manage. It’s basically a collection of Internet-connected devices, which may include PCs, servers, mobile devices, and Internet of Things devices that are infected and controlled by a common type of malware. Users are often unaware of a botnet infecting their system. How can it affect you? Often, the cybercriminal will seek to infect and control thousands, tens of thousands, or even millions of computers, so that the cybercriminal can act as the master of a large ‘zombie network’ or ‘bot-network’ which is capable of delivering a Distributed Denial of Service (DDoS) attack, a large-scale spam campaign, or other types of cyberattack. In some cases, cybercriminals will establish a large network of zombie machines and then sell access to the zombie network to other criminals — either on a rental basis or as an outright sale. Spammers may rent or buy a network in order to operate a large-scale spam campaign. How do botnets work? The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies, or industries. The objective for creating a botnet is to infect as many connected devices as possible, and to use the computing power and resources of those devices for automated tasks that generally remain hidden to the users of the devices. For example, an ad fraud botnet that infects a user’s PC will take over the system’s web browsers to divert fraudulent traffic to certain online advertisements. However, to stay concealed, the botnet won’t take complete control of the web browsers, which would alert the user. Instead, the botnet may use a small portion of the browser’s processes, often running in the background, to send a barely noticeable amount of traffic from the infected device to the targeted ads. On its own, that fraction of bandwidth taken from an individual device won’t offer much to the cybercriminals running the ad fraud campaign. However, a botnet that combines millions of devices will be able to generate a massive amount of fake traffic for ad fraud, while also avoiding detection by the individuals using the devices. Notable botnet attacks The Zeus malware, first detected in 2007, is one of the best-known and widely used malware types in the history of information security. Zeus uses a Trojan horse program to infect vulnerable devices and systems, and variants of this malware have been used for various purposes over the years, including to spread CryptoLocker ransomware. The Srizbi botnet, which was first discovered in 2007, was, for a time, the largest botnet in the world. Srizbi, also known as the Ron Paul spam botnet, was responsible for a massive amount of email spam — as much as 60 billion messages a day, accounting for roughly half of all email spam on the Internet at the time. In 2007, the Srizbi botnet was used to send out political spam emails promoting then-U.S. Presidential candidate Ron Paul. An extensive cybercrime operation and ad fraud botnet known as Methbot was revealed in 2016 by cybersecurity services company White Ops. According to security researchers, Methbot was generating between $3 million and $5 million in fraudulent ad revenue daily last year by producing fraudulent clicks for online ads, as well as fake views of video advertisements. Several powerful, record-setting distributed denial-of-service (DDoS) attacks were observed in late 2016, and they later traced to a new brand of malware known as Mirai. The DDoS traffic was produced by a variety of connected devices, such as wireless routers and CCTV cameras. Preventing botnet attacks In the past, botnet attacks were disrupted by focusing on the command-and-control source. Law enforcement agencies and security vendors would trace the bots’ communications to wherever the C&C servers were hosted, and then force the hosting or service provider to shut them down. There are several measures that users can take to prevent botnet virus infection. Because bot infections usually spread via malware, many of these measures actually focus on preventing malware infections. Recommended practices for botnet prevention include: Network baselining: Network performance and activity should be monitored so that irregular network behavior is apparent. Software patches: All software should be kept up-to-date with security patches. Vigilance: Users should be trained to refrain from activity that puts them at risk of bot infections or other malware. This includes opening emails or messages, downloading attachments, or clicking links from untrusted or unfamiliar sources. Anti-botnet tools: Anti-botnet tools provide botnet detection to augment preventative efforts by finding and blocking bot viruses before infection occurs. Most programs also offer features such as scanning for bot infections and botnet removal as well. Firewalls and antivirus software typically include basic tools for botnet detection, prevention, and removal. Tools like Network Intrusion Detection Systems (NIDS), rootkit detection packages, network sniffers, and specialized anti-bot programs can be used to provide more sophisticated botnet detection/prevention/removal. However, as botnet malware has become more sophisticated, and communications have become decentralized, takedown efforts have shifted away from targeting C&C infrastructures to other approaches. These approaches include identifying and removing botnet malware infections at the source devices, identifying and replicating the peer-to-peer communication methods and, in cases of ad fraud, disrupting the monetization schemes, rather than the technical infrastructures. Preventing botnet attacks has been complicated by the emergence of malware like Mirai, which targets routers and IoT devices that have weak or factory default passwords, and which can be easily compromised. In addition, users may be unable to change the passwords for many IoT devices, which leaves them exposed to attacks. If the manufacturer cannot remotely update the devices’ firmware to patch them or change their hardcoded passwords, then they may have to conduct a factory recall of the affected devices. About the Author Hari Vignesh Jayapalan is a Google Certified Android app developer, IDF Certified UI & UX Professional, street magician, fitness freak, technology enthusiast, and wannabe entrepreneur. He can be found on Twitter @HariofSpades.

The rise of the bots is nigh! If you can imagine a situation involving a dialog, there is probably a chatbot for that. Just look at the chatbot market - text-based email/SMS bots, voice-based bots, bots for customer support, transaction-based bots, entertainment bots and many others. A large number of enterprises, from startups to established organizations, are seeking to invest in this sector. This has also led to an increase in the number of platforms used for chatbot building. These frameworks incorporate AI techniques along with natural language processing capabilities to assist developers in building and deploying chatbots. Let’s start with how a chatbot typically works before diving into some of the frameworks. Understand: The first step for any chatbot is to understand the user input. This is made possible using pattern matching and intent classification techniques. ‘Intents’ are the tasks that users might want to perform with a chatbot. Machine learning, NLP and speech recognition techniques are typically used to identify the intent of the message and extract named entities. Entities are the specific pieces of information extracted from the user’s response i.e. the content associated with an intent. Respond: After understanding, the next goal is to generate a response. This is based on the current input message and the context of the conversation. After specifying the intents and entities, a dialog flow is constructed. This is basically the replies/feedback expected from a chatbot. Learn: Chatbots use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided, and elicit a suitable response for future replies. This is important because different requests might have different meanings depending on previous requests. Top chatbot development frameworks A bot development framework is a set of predefined classes, functions, and utilities that a developer can use to build chatbots easier and faster. They vary in the level of complexity, integration capabilities, and functionalities. Let us look at some of the development platforms utilized for chatbot building. API.AI API.AI, a code based framework with a simple web-based interface, allows users to build engaging voice and text-based conversational apps using a large number of libraries and SDKs including Android, iOS, Webkit HTML5, Node.js, and Python API. It also supports nearly 32 one-click platform integrations such as Google, Facebook Messenger, Twitter and Skype to name a few. API.AI makes use of an agent - a container that transforms natural language based user requests into actionable data. The software tries to find the intent behind a user’s reply and matches it to the default or the closest match. After intent matching, it executes the actions and responses the developer has defined for that intent. API.AI also makes use of entities. Once the intents and entities are specified, the bot is trained. API.AI’s training module efficiently tracks each user’s request and lets developers see how they are parsed and matched to an intent. It also allows for correction of any errors and change requests thus retraining the bot. API.AI streamlines the entire bot-creating process by helping developers provide domain-specific knowledge that is unique to a bot’s needs while working on speech recognition, intent and context management in the backend. Google has recently partnered with API.AI to help them build conversational tools like Apple’s Siri. Microsoft Bot Framework Microsoft Bot Framework allows building and deployment of chatbots across multiple platforms and services such as web, SMS, non-Microsoft platforms, Office 365, Skype etc. The Bot Framework includes two components - The Bot Builder and the Microsoft Cognitive Services. The Bot Builder comprises of two full-featured SDKs - for the.NET and the Node.js platforms along with an emulator for testing and debugging. There’s also a set of RESTful APIs for building code in other languages. The SDKs support features for simple and easy interactions between bots. They also have a large collection of prebuilt sample bots for the developer to choose from. The Microsoft Cognitive Services is a collection of intelligent APIs that simplify a variety of AI tasks such as allowing the system to understand and interpret the user's needs using natural language in just a few lines of code. These APIs allow integration to most modern languages and platforms and constantly improve, learn, and get smarter. Microsoft created the AI Inner Circle Partner Program to work hand in hand with industry to create AI solutions. Their only partner in the UK is ICS.AI who build conversational AI solutions for the UK's public sector. ICS are the first choice for many organisations due to their smart solutions that scale and serve to improve services for the general public. Developers can build bots in the Bot Builder SDK using C# or Node.js. They can then add AI capabilities with Cognitive Services. Finally, they can register the bots on the developer portal, connecting it to users across platforms such as Facebook and Microsoft Teams and also deploy it on the cloud like Microsoft Azure. For a step-by-step guide for chatbot building using Microsoft Bot Framework, you can refer to one of our books on the topic. Sabre Corporation, a customer service provider for travel agencies, have recently announced the development of an AI-powered chatbot that leverages Microsoft Bot Framework and Microsoft Cognitive Services. Watson Conversation IBM’s Watson Conversation helps build chatbot solutions that understand natural-language input and use machine learning to respond to customers in a way that simulates conversations between humans. It is built on a neural network of one million Wikipedia words. It offers deployment across a variety of platforms including mobile devices, messaging platforms, and robots. The platform is robust and secure as IBM allows users to opt out of data sharing. The IBM Watson Tone Analyzer service can help bots understand the tone of the user’s input for better management of the experience. The basic steps to create a chatbot using Watson Conversation are as follows. We first create a workspace - a place for configuring information to maintain separate intents, user examples, entities, and dialogues for each application. One workspace corresponds to one bot. Next, we create Intents. Watson Conversation makes use of multiple conditioned responses to distinguish between similar intents. For example, instead of building specific intents for locations of different places, it creates a general intent “location” and adds an entity to capture the response, like the “location- bedroom” - to the right, near the stairs, “location-kitchen”- to the left. The third step is entity establishment. This involves grouping entities that might trigger a similar response in the dialog. The dialog flow, thus generated after specifying the intents and entities, goes through testing followed by embedding this into an application. It is then connected with other services by using the conversation API. Staples, an office supply retailing firm, uses Watson Conversation in their “Easy Systems” to simplify the customer’s shopping experience. CXP Designer and Aspect NLU Aspect Customer Experience Platform is an application lifecycle management tool to build text and voice-based applications such as chatbots. It provides deployment options across multiple communication channels like text, voice, mobile web and social media networks. The Aspect CXP typically includes a CXP designer to build chatbots and the inbuilt Aspect NLU to provide advanced natural language capabilities. CXP designer works by creating dialog objects to provide a menu of options for frontend as well as backend. Menu items for the frontend are used to create intents and modules within those intents. The developer can then modify labels (of those intents and modules) manually or use the Aspect NLU to disambiguate similar questions for successful extraction of meaning and intent. The Aspect NLU includes tools for spelling correction, linguistic lexicons such as nouns, verbs etc. and options for detecting and extracting common data types such as date, time, numbers, etc. It also allows a developer to modify the meaning extraction based on how they want it if they want it! CXP designer also allows skipping of certain steps in chatbots. For instance, if the user has already provided their tracking id for a particular package, the chatbot will skip the prompt of asking them the tracking id again. With Aspect CXP, developers can create and deploy complex chatbots. Radisson Blu Edwardian, a hotel in London, has collaborated with Aspect software to build an SMS based, AI virtual host. Conclusion Another popular chatbot development platform worth mentioning is the Facebook messenger with over 100,000 monthly active bots, but without cross-platform deployment features. The above bot frameworks are typically used by developers to build chatbots from scratch and require some programming skills. However, there has been a rise in automated bot development tools of late. Some of these include Chatfuel and Motion AI and typically involve drag and drop functionalities. With such tools, beginners and non-programmers can create and deploy chatbots within few minutes. But, they lack the extended functionalities supported by typical code based frameworks such as the flexibility to store data, produce analytics or incorporate customized AI tasks. Every chatbot development system, whether framework or tool, serves a different purpose. Choosing the right one depends on the type of application to build, organizational needs, and the developer’s expertise.

We are a species obsessed with ‘intelligence’ since gaining consciousness. We have always been inventing ways to make our lives better through sheer imagination and application of our intelligence. Now, it comes as no surprise that we want our modern day creations to be smart as well - be it a web app or a mobile app. The first question that comes to mind then is what makes an application ‘intelligent’? A simple answer for budding developers is that intelligent apps are apps that can take intuitive decisions or provide customized recommendations/experience to their users based on insights drawn from data collected from their interaction with humans. This brings up a whole set of new questions: How can intelligent apps be implemented, what are the challenges, what are the primary application areas of these so-called Intelligent apps, and so on. Let’s start with the first question. How can intelligence be infused into an app? The answer has many layers just like an app does. The monumental growth in data science and its underlying data infrastructure has allowed machines to process, segregate and analyze huge volumes of data in limited time. Now, it looks set to enable machines to glean meaningful patterns and insights from the very same data. One such interesting example is predicting user behavior patterns. Like predicting what movies or food or brand of clothing the user might be interested in, what songs they might like to listen to at different times of their day and so on. These are, of course, on the simpler side of the spectrum of intelligent tasks that we would like our apps to perform. Many apps currently by Amazon, Google, Apple, and others are implementing and perfecting these tasks on a day-to-day basis. Complex tasks are a series of simple tasks performed in an intelligent manner. One such complex task would be the ability to perform facial recognition, speech recognition and then use it to perform relevant daily tasks, be it at home or in the workplace. This is where we enter the realm of science fiction where your mobile app would recognise your voice command while you are driving back home and sends automated instructions to different home appliances, like your microwave, AC, and your PC so that your food is served hot when you reach home, your room is set at just the right temperature and your PC has automatically opened the next project you would like to work on. All that happens while you enter your home keys-free thanks to a facial recognition software that can map your face and ID you with more than 90% accuracy, even in low lighting conditions. APIs like IBM Watson, AT&T Speech, Google Speech API, the Microsoft Face API and some others provide developers with tools to incorporate features such as those listed above, in their apps to create smarter apps. It sounds almost magical! But is it that simple? This brings us to the next question. What are some developmental challenges for an intelligent app? The challenges are different for both web and mobile apps. Challenges for intelligent web apps For web apps, choosing the right mix of algorithms and APIs that can implement your machine learning code into a working web app, is the primary challenge. plenty of Web APIs like IBM Watson, AT&T speech etc. are available to do this. But not all APIs can perform all the complex tasks we discussed earlier. Suppose you want an app that successfully performs both voice and speech recognition and then also performs reinforcement learning by learning from your interaction with it. You will have to use multiple APIs to achieve this. Their integration into a single app then becomes a key challenge. Here is why. Every API has its own data transfer protocols and backend integration requirements and challenges. Thus, our backend requirement increases significantly, both in terms of data persistence and dynamic data availability and security. Also, the fact that each of these smart apps would need customized user interface designs, poses a challenge to the front end developer. The challenge is to make a user interface so fluid and adaptive that it supports the different preferences of different smart apps. Clearly, putting together a smart web app is no child’s play. That’s why, perhaps, smart voice-controlled apps like Alexa are still merely working as assistants and providing only predefined solutions to you. Their ability to execute complex voice-based tasks and commands is fairly low, let alone perform any non-voice command based task. Challenges for intelligent mobile apps For intelligent mobile apps, the challenges are manifold. A key reason is network dependency for data transfer. Although the advent of 4G and 5G mobile networks has greatly improved mobile network speed, the availability of network and the data transfer speeds still pose a major challenge. This is due to the high volumes of data that intelligent mobile apps require to perform efficiently. To circumvent this limitation, vendors like Google are trying to implement smarter APIs in the mobile’s local storage. But this approach requires a huge increase in the mobile chip’s computation capabilities - something that’s not currently available. Maybe that’s why Google has also hinted at jumping into the chip manufacturing business if their computation needs are not met. Apart from these issues, running multiple intelligent apps at the same time would also require a significant increase in the battery life of mobile devices. Finally, comes the last question. What are some key applications of intelligent apps? We have explored some areas of application in the previous sections keeping our focus on just web and mobile apps. Broadly speaking, whatever makes our daily life easier, is ideally a potential application area for intelligent apps. From controlling the AC temperature automatically to controlling the oven and microwave remotely using the vacuum cleaner (of course the vacuum cleaner has to have robotic AI capabilities) to driving the car, everything falls in the domain of intelligent apps. The real questions for us are What can we achieve with our modern computation resources and our data handling capabilities? How can mobile computation capabilities and chip architecture be improved drastically so that we can have smart apps perform complex tasks faster and ease our daily workflow? Only the future holds the answer. We are rooting for the day when we will rise to become a smarter race by delegating lesser important yet intelligent tasks to our smarter systems by creating intelligent web and mobile apps efficiently and effectively. The culmination of these apps along with hardware driven AI systems could eventually lead to independent smart systems - a topic we will explore in the coming days.

When Google decided to design their own chip with TPU, it generated a lot of buzz for faster and smarter computations with its ASIC-based architecture. Google claimed its move would significantly enable intelligent apps to take over, and industry experts somehow believed a reply from Microsoft was always coming (remember Bing?). Well, Microsoft has announced its arrival into the game – with its own real-time AI-enabled chip called Brainwave. Interestingly, as the two tech giants compete in chip manufacturing, developers are certainly going to have more options now, while facing the complex computational processes of modern day systems. What is Brainwave? Until recently, Nvidia was the dominant market player in the microchip segment, creating GPUs (Graphics Processing Unit) for faster processing and computation. But after Google disrupted the trend with its TPU (tensor processing unit) processor, the surprise package in the market has come from Microsoft. More so because its ‘real-time data processing’ Brainwave chip claims to be faster than the Google chip (the TPU 2.0 or the Cloud TPU chip). The one thing that is common between both Google and Microsoft chips is that they can both train and simulate deep neural networks much faster than any of the existing chips. The fact that Microsoft has claimed that Brainwave supports Real-Time AI systems with minimal lag, by itself raises an interesting question - are we looking at a new revolution in the microchip industry? The answer perhaps lies in the inherent methodology and architecture of both these chips (TPU and Brainwave) and the way they function. What are the practical challenges of implementing them in real-world applications? The Brainwave Architecture: Move over GPU, DPU is here In case you are wondering what the hype with Microsoft’s Brainwave chip is about, the answer lies directly in its architecture and design. The present-day complex computational standards are defined by high-end games for which GPUs (Graphical Processing Units) were originally designed. Brainwave differs completely from the GPU architecture: the core components of a Brainwave chip are Field Programmable Gate Arrays or FPGAs. Microsoft has developed a huge number of FPGA modules on top of which DNN (Deep Neural Network) layers are synthesized. Together, this setup can be compared with something similar to Hardware Microservices where each task is assigned by a software to different FPGA and DNN modules. These software controlled Modules are called DNN Processing Units or DPUs. This eliminates the latency of the CPU and the need for data transfer to and fro from the backend. The two methodologies involved here are seemingly different in their architecture and application: one is the hard DPU and the other is the Soft DPU. While Microsoft has used the soft DPU approach where the allocation of memory modules are determined by software and the volume of data at the time of processing, the hard DPU has a predefined memory allocation which doesn’t allow for flexibility so vital in real-time processing. The software controlled feature is exclusive to Microsoft, and unlike other AI processing chips, Microsoft have developed their own easy to process data types that are faster to process. This enables the Brainwave chip to perform near real-time AI computations easily.  Thus, in a way Microsoft brainwave holds an edge over the Google TPU when it comes to real-time decision making and computation capabilities. Brainwave’s edge over TPU 2 - Is it real time? The reason Google had ventured out into designing their own chips was their need to increase the number of data centers, with the increase in user queries. They had realized the fact that instead of running data queries via data centers, it would be far more plausible if the computation was performed in the native system. That’s where they needed more computational capabilities than what the modern day market leaders like Intel X86 Xeon processors and the Nvidia Tesla K80 GPUs offered. But Google opted for Application Specific Integrated Circuits (ASIC) instead of FPGAs, the reason being that it was completely customizable. It was not specific for one particular Neural Network but was rather applicable for multiple Networks. The trade-off for this ability to run multiple Neural Networks was of course Real Time computation which Brainwave could achieve because of using the DPU architecture. The initial data released by Microsoft shows that the Brainwave has a data transfer bandwidth of 20TB/sec, 20 times faster than the latest Nvidia GPU chip. Also, the energy efficiency of Brainwave is claimed to be 4.5 times better than the current chips. Whether Google would up their ante and improve on the existing TPU architecture to make it suitable for real-time computation is something only time can tell. [caption id="attachment_1064" align="alignnone" width="644"] Source: Brainwave_HOTCHIPS2017 PPT on Microsoft Research Blog[/caption] Future outlook and challenges Microsoft is yet to declare the benchmarking results for the Brainwave chip. But Microsoft Azure customers most definitely look forward to the availability of Brainwave chip for faster and better computational abilities. What is even more promising is Brainwave works seamlessly with Google’s TensorFlow and Microsoft’s own CNTK framework. Tech startups like Rigetti, Mythic and Waves are trying to create mainstream applications which will employ AI and quantum computation techniques. This will bring AI to the masses, by creating practical AI driven applications for daily consumers, and these companies have shown a keen interest in both the Microsoft and the Google AI chips. In fact, Brainwave will be most suited for these companies such as the above which are looking to use AI capabilities for everyday tasks, as they are less in number because of the limited computational capabilities of the current chips. The challenges with all AI chips, including Brainwave, will still revolve around their data handling capabilities, the reliability of performance, and on improving memory capabilities of our current hardware systems.

Are you a proud machine learning engineer who hates that the job tests your limits as a human being? Do you dread the long hours of data experimentation and data modeling that leave you high and dry? Automated Machine Learning or AutoML can put that smile back on your face. A self-replicating AI algorithm, AutoML is the latest tool that is being applied in the real world today, and AI market leaders such as Google have made a significant investment to research further in this field. AutoML has seen a steep rise in research and new tools over the last couple of years, but its recent mention during Google IO 2017 has piqued the interest of the entire developer community. What is AutoML all about and what makes it so interesting? Evolution of automated machine learning Before we try to understand AutoML, let’s look at what triggered the need for automated machine learning. Until now, building machine learning models that work in the real world has been a domain ruled by researchers, scientists, and machine learning experts. The process of manually designing a machine learning model involves several complex and time-consuming steps such as: Pre-processing data Selecting appropriate ML architecture Optimizing hyperparameters Constructing models Evaluating suitability of models Add to this, the several layers of neural networks required for an efficient ML architecture -- an n-layer neural network could result in nn potential networks. This level of complexity could be overwhelming for the millions of developers who are keen on embracing machine learning. AutoML tries to solve this problem of complexity and makes machine learning accessible to a large group of developers by automating routine but complex tasks such as the design of neural networks. Since this cuts down development time significantly and takes care of several complex tasks involved in building machine learning models, AutoML is expected to play a crucial role in bringing machine learning to the mainstream. Approaches to automating model generation   With a growing body of research, AutoML aims to automate the following tasks in the field of machine learning: Model Selection Parameter Tuning Meta Learning Ensemble Construction It does this by using a wide range of algorithms and approaches such as: Bayesian Optimization: One of the fundamental approaches for automating model generation is to use Bayesian methods for hyperparameter tuning. By modeling the uncertainty of parameter performance, different variations of the model can be explored which offers an optimal solution. Meta-learning and Ensemble Construction: To further increase AutoML efficiency, meta-learning techniques are used to find and pick optimal hyperparameter settings. These techniques can be further coupled with auto-ensemble construction techniques to create effective ensemble model from a collection of models that undergo optimization. Using these techniques, a high level of accuracy can be achieved throughout the process of automated generation of models. Genetic Programming: Certain tools like TPOT also make use of a variation of genetic programming (tree-based pipeline optimization) to automatically design and optimize ML models that offer highly accurate results for a given set of data. This approach makes use of operators at various stages of the data pipeline which are assembled together in the form of a tree-based pipeline. These are then further optimized and newer pipelines are auto-generated using genetic programming. If these weren’t enough, Google in its recent posts disclosed that they are using reinforcement learning approach to give a further push to develop efficient AutoML techniques. What are some tools in this area? Although it’s still early days, we can already see some frameworks emerging to automate the generation of your machine learning models.   Auto-sklearn: Auto-sklearn, the tool which won the ChaLearn AutoML Challenge, provides a wrapper around the popular Python library scikit-learn to automate machine learning. This is a great addition to the ever-growing ecosystem of Python data science tools. Built on top of Bayesian optimization, it takes away the hassle of algorithm selection, parameter tuning, and ensemble construction while building machine learning pipelines. With auto-sklearn, developers can create rapid iterations and refinements to their machine learning models, thereby saving a significant amount of development time. The tool is still in its early stages of development, so expect a few hiccups while using it. DataRobot: DataRobot offers a machine learning automation platform to all levels of data scientists aimed at significantly reducing the time to build and deploy predictive models. Since it’s a cloud platform it offers great power and speed throughout the process of automating the model generation process. In addition to automating the development of predictive models, it offers other useful features such as a web-based interface, compatibility with several leading tools such as Hadoop and Spark, scalability, and rapid deployment. It’s one of those few machine learning automation platforms which are ready for industry use. TPOT: TPOT is yet another Python tool meant for automated machine learning. It uses a genetic programming approach to iterate and optimize machine learning models. As in the case of auto-sklearn, TPOT is also built on top of scikit-learn. It has a growing interest level on GitHub with 2400 stars and has observed a 100% rise in the past one year alone. Its goals, however, are quite similar to those of Auto-sklearn: feature construction, feature selection, model selection, and parameter optimization. With these goals in mind, TPOT aims at building efficient machine learning systems in lesser time and with better accuracy. Will automated machine learning replace developers? AutoML as a concept is still in its infancy. But as market leaders like Google, Facebook, and others research more in this field, AutoML will keep evolving at a brisk pace. Assuming that AutoML would replace humans in the field of data science, however, is a far-fetched thought and nowhere near reality. Here is why. AutoML as a technique is meant to make the neural network design process efficient rather than replace humans and researchers in the field of building neural networks. The primary goal of AutoML is to help experienced data scientists be more efficient at their work i.e., enhance productivity by a huge margin and to reduce the steep learning curve for the many developers who are keen on designing ML models - i.e., make ML more accessible. With the advancements in this field, it’s exciting times for developers to embrace machine learning and start building intelligent applications. We see automated machine learning as a game changer with the power to truly democratize the building of AI apps. With automated machine learning, you don’t have to be a data scientist to develop an elegant AI app!

Virtual machines and containers are pretty similar, but they do possess some important differences. These differences will dictate which ones you decide to use. So, when you ask a question like 'virtual machines vs containers' there isn't necessarily going to be an outright winner - but there might be a winner for you in a given scenario. Let's take a look at what a virtual machine is, exactly, what a container is, and how they compare - as well as the key differences between the two. What is a virtual machine? Virtual machines are a product of hardware virtualization. They sit on top of physical machines with the hypervisor or virtual machine manager in between, acting as a layer of abstraction between the virtual machine and the underlying hardware. A virtualized physical machine can host multiple virtual machines, enabling better hardware utilization. Since the hypervisor abstracts the physical machine's hardware, it allows virtual machines to use a different operating system on the same host machine. The host operating system and virtual machine operating system run their own kernel. All the communication between the virtual machines and the host machine occurs through the hypervisor, resulting in high level of isolation. This means if one virtual machine crashes, it would not affect other virtual machines running on the same physical machine. Although the hypervisor's abstraction layer offers a high level of isolation, it also affects the performance. This problem can be solved by using a different virtualization technique. What is a container? Containers use lightweight operating system level virtualization. Similar to virtual machines, multiple containers can run on the same host machine. However, containers do not have their own kernel. They share the host machine's kernel, making them much smaller in size compared to virtual machines. They use process level isolation, allowing processes inside a container to be isolated from other containers. The difference between virtual machines and containers In his post Containers are not VMs, Mike Coleman use the analogy of houses and apartment buildings to compare virtual machines and containers. Self-contained houses have their own infrastructure while apartments are built around shared infrastructure. Similarly, virtual machines have their own operating system, with kernel, binaries, libraries etc. While containers share the host operating system kernel with other containers. Due to this, containers are much smaller in size allowing a physical machine to host more containers than virtual machines. Since containers use lightweight operating system level virtualization instead of a hypervisor, they are less resource intensive compared to virtual machines and offer better performance. Compared to virtual machines, containers are faster, quicker to provision, and easy to scale. As spinning a new container is quick and easy when a patch or an update is required, it is easy to start a new container and stop the old one instead of updating a running container. This allows us to build immutable infrastructure, which is reliable, portable and easy to scale. All of this makes containers a preferred choice for application deployment, especially with the teams that are using micro-services or similar architecture, where an application is composed of multiple small services instead of a monolith. In microservice architecture, an application is built as a suite of independent, self-contained services. This allows the teams to work independent of each other and deliver features quicker. However, decomposing applications into multiple parts adds operational complexity and overhead. Containers solve this problem. Containers can serve as a building block in the microservice world where each service can be packaged and deployed as a container. A container will have everything that is required to run a service, this includes service code, its dependencies, configuration files, libraries etc. Packaging a service and all its dependencies as a container makes it easy to distribute and deploy a service. Since the container includes everything that is required to run a service, it can be deployed reliably in different environments. A service packaged as a container will run the same way locally on a developer's machine, in a test environment, and in production. However, there are things to consider when using containers. Containers share the kernel and other components of the host operating system. This makes them less isolated compared to virtual machines, and thus less secure. Since each virtual machine has its own kernel, we can run virtual machines with a different operating system on the same physical machine. However since containers share the host operating system kernel, only the guest operating system that can work with the host operating system can be installed in a container. Virtual machines vs containers - in conclusion... Compared to virtual machines, containers are lightweight, performant and easy to provision. While containers seem to be the obvious choice to build and deploy applications, virtual machines have their own advantages. Compared to physical machines, virtual machines have the better tooling and are easier to automate. Virtual machines and containers can co-exist. Organizations with existing infrastructure built around virtual machines can take the benefits of containers by deploying them on virtual machines.

The open source software movement has sparked an incredibly rich community of collaborative software developers producing wave after wave of applications. What started as a lofty ideal has become the norm. As many as 93 percent of organizations use open source software and 78 percent run part or all of their operations on it, according to The Tenth Annual Future of Open Source Survey.  Most open source software projects come to life because someone is trying to scratch an itch. A  group of coders, or a team of academics, or a fast-moving startup will build some software that solves a very real computing problem, and then they’ll open source the code, sharing it with the world at large. Maybe the coders are trying to help the larger world of software developers, believing that others will find the code useful too. Maybe they’re trying to get more eyes on their code, hoping that others will contribute bug reports and fixes to the project. Or maybe, as is typically the case, they’re trying to do both.  The popular data-crunching tool Hadoop is a great example. Doug Cutting and Mike Cafarella started Hadoop to solve scalability problems they had with their open source search engine software, Nutch. Then Yahoo saw the work they were doing, realized it would be useful, and hired Cutting to develop it further. Soon, other companies like Facebook and eBay joined in as well. Today, Hadoop is used by countless companies to crunch data, and several commercial outfits have sprung up to support and develop the software and its ecosystem.  There are a nearly endless number of open source projects that have evolved along similar lines, including the Apache web server, the Ruby on Rails programming framework, and, of course, the Linux operating system. But in recent years, we’re seeing many old-school tech companies– companies that predate the recent open source revolution – use open source in very different ways. Now, companies like Microsoft, Cisco, and Salesforce are creating new open source projects, mainly as a means of promoting new or existing products and services. But by adopting open source projects, will it really make the training of your team hard? Well, it really depends on the type of project that you decide to go with. Your team’s learning curve will be high if you fail in the below metrics. Choosing the type of OSS There are different types of OSS; it can be as small as a plugin, or a small library to the enterprise application itself. So if it’s a library or plugin, which performs one or two functions, or features in your product, it is really advisable to go with it. Because, if you need a small modification or something to be built on top of it, not much training is required — unless it’s a poorly rated library.  But if you want to go with big enterprise software, you need to think more than twice before adopting it. If you’re selling a product, it’s advisable to build from scratch, so that you will have more control over it. If not, if it’s for internal use, you can clearly go for OSS — no matter how bad it looks or behaves, only internal employees will be using it. Developer’s mindset Many developers will not wish to work in a legacy code base. They always prefer to start from scratch. But if they are exposed to a legacy code base, on top of some other OSS, they won't be pleased. Definitely, the learning curve will be more because they need to understand the current system, and on top of that, they need to understand the OSS codebase as well and how it’s tailored to the current system.  If the OSS is a famous one like Hadoop, where the support is enormous and the developer community is quite active, you will have more developers who are exposed to the software and you will definitely get skilled professionals to tailor it to your needs. But if the OSS is not famous, your team will face a greater learning curve. Be prepared for patches and updates Updates and patches will fly continuously if the project is active. Software is never 100 percent perfect and as it is OSS, the team availability will be less and bug fixes won't be immediate. So you need a lot of patience, and there is the possibility that a bigger update will roll out as well,  which you should not let affect your tailored modules. On those occasions, your team’s learning curve will be high.  As discussed previously, software support really matters. There are a few OSS companies who provide support for free or for cost as well . This helps to keep your development cost and the learning curve spike to a minimum. But if the support does not exist, you have no choice but to be patient with your tech team. They really need time to understand the system and tailor it.  About the Author  HariVigneshJayapalan is a Google Certified Android app developer, IDF Certified UI & UX Professional, street magician, fitness freak, technology enthusiast, and wannabe entrepreneur. He can be found on Twitter @HariofSpades. 

“I propose to consider the question - Can machines think?” - Alan Turing The goal for AI research has always remained the same - create a machine that has human-like decision-making capabilities based on available information. This includes the machine’s ability to analyze and process huge amounts of data and then make a meaningful inference from it. Machine learning, deep learning and other old and new paradigms in AI research are all attempts at imparting complex decision-making capabilities to machines or systems. Alan Turing’s famous test for AI has set the standards over the years for what qualifies as a smart AI i.e. a thinking machine. The imitation game is about an AI/ bot interacting with a human anonymously, in a way that the human can’t decipher the fact that it’s a machine. This not-so-trivial test has seen many adaptations over the years like the modern day Tokyo test. These tests set challenging boundaries that machines must cross to be considered capable of possessing intelligence. Neuroevolution, a few decades old theory, remodeled in a modern day format with the help of Neural and Deep Neural Networks, promises to challenge these boundaries and even break them. With neuroevolution, machines aim to solve complex problems on their own with satisfactory levels of accuracy even though they do not know how to achieve those results.   Neuroevolution: The Essence “If a wild animal habitually performs some useless activity, natural selection will favor rival individuals who instead devote time to surviving and reproducing...Ruthless utilitarianism trumps, even if it doesn’t always seem that way.” - Richard Dawkins This is the essence of Neuroevolution. But the process itself is not as simple. Just like the human evolution process, in the beginning, a set of algorithms work on a problem. The algorithms that show an inclination to solve the problem in the right way are selected for the next stage. They then undergo random minor mutations - i.e., small logical changes in the inherent algorithm structure. Next, we check whether these changes enable the algorithms to achieve the same result with better accuracy or efficiency. The successful ones then move to the next stage with further mutations introduced. This is similar to how nature did the sorting for us and humans evolved from a natural need to survive in unfamiliar situations. Since the concept uses Neural Networks, it has come to be known as Neuroevolution. Neuroevolution, in the simplest terms, is the process of “descent with modification” by which machines/systems evolve and get better at solving the problems they were built for. Backpropagation to DNN: The Evolution Neural networks are made up of nodes. These nodes function like neurons in the human brain that receive a set of inputs and generate a response based on the type, intensity, frequency etc of stimuli. A single node looks like the below illustration: An algorithm can be viewed as a node. With backpropagation, the algorithm is modified in an iterative manner - where the error generated after each pass, is fed back to the system. The algorithms (nodes) responsible for higher error contribution are identified and assigned less weight in the next pass. Thus, backpropagation is a way to assign appropriate weights to nodes by calculating error contributions of individual nodes. These nodes, when combined in different layers, form the structure of Deep Neural Networks. Deep Neural networks have separate input and output layers and a middle layer of hidden nodes which form the core of DNN. This hidden layer consists of multiple nodes like the following. In case of DNNs, as before in each iteration, the weight of the nodes are adjusted based on their accuracy. The number of iterations is a factor that varies for each DNN. As explained earlier, the system without any external stimuli continues to improve on its own. Now, where have we seen this before? Of course, this looks a lot like a simplified, miniature version of evolution! Unfit nodes are culled by reducing the weight they have in the overall output, and the ones with favorable results are encouraged, just like the natural selection. However, the only thing that is missing from this is the mutation and the ability to process mutation. This is where we introduce the mutations in the successful algorithms and let them evolve on their own. Backpropagation in DNNs doesn’t change the algorithm or it’s approach, it merely increases or decreases the algorithm’s overall contribution to the desired result. Forcing random mutations of neural and deep neural networks and then letting these mutations take shape as these neural networks together try to solve a given problem seem pretty straightforward. The point where everything starts getting messy is when different layers or neural networks start solving the given problem in their own pre-defined way. One of two things may then happen: The neural networks behave in self-contradiction and stall the overall problem-solving process. The system as such cannot take any decision and becomes dormant.     The neural networks are in some sort of agreement regarding a decision. The decision itself might be correct or incorrect. Both scenarios present us with dilemmas - how to restart a stalled process and how to achieve better decision making capability. The solution to both of situations lies in enabling the DNNs to rectify themselves first by choosing the correct algorithms. And then by mutating them with an intention to allow them to evolve and reach a decision toward achieving greater accuracy.   Here’s a look at some popular implementations of this idea. Neuroevolution in flesh and blood Cutting edge AI research giants like OpenAI backed by Elon Musk and Google DeepMind have taken the concept of neuroevolution and applied them to a bunch of deep neural networks. Both aim to evolve these algorithms in a way that the smarter ones survive and eventually create better and faster models & systems. Their approaches are however starkly different. The Google implementation Google’s way is simple - It takes a number of algorithms, divides them into groups and assigns one particular task to all. The algorithms that fare better at solving these problems are then chosen for the next stage, much like the reward and punishment system in reinforcement learning. However, the difference here is that the faster algorithms are not just chosen for the next step, but their models and parameters are tweaked slightly -  this is our way of introducing a mutation into the successful algorithms. These minor mutations then play out as these modified algorithms try to solve the given problem. Again, the better ones remain and the rest are culled out. This way, the algorithms themselves find a way to perform better and better until they are reasonably close to the desired result. The most important advantage of this process is that the algorithms keep track of their evolution process as they get smarter. A major limitation of Google’s approach is that the time taken for performing these complex computations is too high, hence the result takes time to show. Also, once the mutation kicks in, their behavior is not controlled externally - i.e., quite literally they can go berserk because of the resulting mutation - which means the process can fail even at an advanced stage. The OpenAI implementation Let’s contrast this with OpenAI’s master-worker approach to neuroevolution. OpenAI used a set of nearly 1440 algorithms to play the game of Atari and submit their scores to the master algorithm. Then, the algorithms with better scores were chosen and given a mutation and put back into the same process. In more abstract terms, the OpenAI method looks like this. A set of worker algorithms are given a certain complex problem to solve. The best scores are passed on to the master algorithm. The better algorithms are then mutated and set to perform the same tasks. The scores are again recorded and passed on to the master algorithm. This happens through multiple iterations. The master algorithm progressively eliminates the chance of failure since the master algorithm knows which algorithms to employ when given a certain problem. However, it does not know the road to success as it has access only to the final scores and not how those scores were achieved. The advantage of this approach is that better results are guaranteed, there are no cases of decision conflict and the system stalling. The flip side is that this system only knows its way through the given problem. All this effort to evolve the system to a better one will have to be repeated for a similar but different problem. The process is therefore cumbersome and lengthy. The Future with Neuroevolution Human evolution has taken millions of years to reach where we are today. Evolving AI and enabling them to pass the Turing test, or to further make them smart enough to pass a university entrance exam will require significant improvement from the current crop of AI. Amazon’s Alexa and Apple’s Siri are mere digital assistants. If we want AI driven smart systems with seamless integration of AI into our everyday life, algorithms with evolutionary characteristics are a must. Neuroevolution might hold the secret to inventing smart AIs that can ultimately propel human civilization to greater heights of development and advancement. “It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers...They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control." - Alan Turing

What are microservices? In terms of software, ‘services’ could be thought of as little chunks of functionality. Services are a part of service-oriented architecture (SOA). Services are stateless, adhere to a contract (shared standards), are autonomous, relatively granular, and should be a ‘black-box’ for the user. Microservices are a logical extension of services. Microservices are services that perform only one function. This matches the Unix philosophy, “Do one thing, and do it well.” But who cares? And what about DevOps? Well, although the title is a cliche, DevOps and microservices based architecture are absolutely a match made in heaven. Following the philosophy of fully explaining terms, let's talk a bit about DevOps. What is DevOps? DevOps, which come from the words "development operations," is a process that is used to create software. DevOps is not one specific philosophy or process, there are many variants, but there are some shared features across most of the variants. DevOps advocates for a continuous development process where as many elements of this process are as automated as possible. Each iteration of a product is coded, built, tested, packaged, and released, and then monitored. This is referred to as the DevOps toolchain. When there is a need or desire to upgrade or change functionality or the way a product is designed, the process begins again. The idea is that DevOps should run in a circular fashion, always upgrading and always getting better. There are myriad tools in use right now within various flavors of DevOps. These tool are designed to meld with the DevOps tool chain and have revolutionized the development process for many developers and companies. Why microservices and DevOps go together You should already see why microservices and DevOps go together so well. DevOps calls for continuous monitoring, testing, and deployment of software. Microservices are inherently modular, because they are intended to perform a single function. Software that is modular easily fits into the DevOps structure. Incremental changes can be made to parts of a project, perhaps a single microservice. If the service contracts and control mechanisms are properly created, a single microservice should be able to be easily upgraded, built, tested, deployed and monitored without sending a cascading wave of bugs through adjacent services. DevOps really doesn’t make much sense outside of a structure like this. If your software is designed as a behemoth interconnected, interdependent ball of wax, changing part of the functionality will ‘break’ everything. This means that as changes or upgrades to a software are made, almost every change, no matter how big or small, will trigger what amounts to an almost full rewrite, or upgrade of the software in question. When applied to this kind of project, most DevOps processes would actually hinder the development process instead of helping. When projects are modularized at a relatively granular level, such as when a project is employing a microservice based structure, DevOps expedites delivery time and quality simultaneously. It should be noted that both a microservice architecture and DevOps processes are not tied to any specific tools or languages. These are philosophies for development and could be applied many different ways. That being said, there are many continuous integration and deployment tools, as well as, many different automation tools that are designed for use within a DevOps framework. Criticisms of microservices There are some criticisms of the microservice structure. One criticism is that using microservices does not get rid of the complexities of a traditional program. Those complexities are just moved onto the network that the services are using to communicate. Stress to the network is another criticism of the microservice architecture. This is because the service architecture distributes the elements of a program or process around a network in various places. In order for these services to perform cohesive functions, they must utilize the network to communicate. Depending on how many services make up a program or process, this could translate into significant network activity, which then creates problems of its own. Another criticism is that microservices can sometimes become, so called, ‘nanoservices.’ This is a service that performs a function so small that cost of the service actually outweighs the utility of the service. These criticisms should be kept in mind, but, in my opinion, they don’t amount to enough to impact the usual functionality of microservices in a DevOps environment. Using microservices in the DevOps process helps fully realize the potential of continuous integration, testing a deployment promised by DevOps. These tools combined can optimize computing in a manner that should be utilized during development whenever possible.