Machine Learning for Web Data
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This document summarizes Hilary Mason's presentation on machine learning for web data. It discusses how machine learning allows algorithms to model and understand data in new ways. Examples include spam filters, Netflix recommendations, language identification, and entity disambiguation. The document also outlines the typical machine learning process of obtaining data, scrubbing it, exploring it to find patterns, modeling it to make predictions, and interpreting the results.
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You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante We live in an ever evolving landscape for cyber threats creating security risk for your production systems. Mitigating these risks requires participation throughout all stages from development through production delivery - and by every role including architects, developers QA and DevOps engineers, product owners and leadership. No one is excused! This session will cover examples of common mistakes or missed opportunities that can lead to vulnerabilities in production - and ways to do better throughout the development lifecycle.
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand IBM LTO10
" How to survive with 1 billion vectors and not sell a kidney: our low-cost c...
" How to survive with 1 billion vectors and not sell a kidney: our low-cost c...Fwdays Let's talk about our history. How we started the project with a small vector database of less than 2 million records. Later, we received a request for +100 million records, then another +100... And so gradually we reached almost 1 billion. Standard tools were quickly running out of steam - we were running into performance, index size, and very limited resources. After a long series of trials and errors, we built our own low-cost cluster, which today stably processes thousands of queries to more than 1B vectors.
Curietech AI in action - Accelerate MuleSoft development
Curietech AI in action - Accelerate MuleSoft developmentshyamraj55 CurieTech AI in Action – Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
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Key Highlights:
Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
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DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes
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Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development
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Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
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Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
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Why Now?
Achieve 10× productivity gains, slashing development time from hours to minutes
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Maintain high accuracy with code quality matching or exceeding manual efforts
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Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency
Conclusion:
CurieTech AI transforms MuleSoft development into an AI-accelerated workflow—letting you focus on innovation, not repetition.
"How to survive Black Friday: preparing e-commerce for a peak season", Yurii ...
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AI Agents and FME: A How-to Guide on Generating Synthetic Metadata
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10 Key Challenges for AI within the EU Data Protection Framework.pdf
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The Future of Product Management in AI ERA.pdf
The Future of Product Management in AI ERA.pdfAlyona Owens Hi, I’m Aly Owens, I have a special pleasure to stand here as over a decade ago I graduated from CityU as an international student with an MBA program. I enjoyed the diversity of the school, ability to work and study, the network that came with being here, and of course the price tag for students here has always been more affordable than most around.
Since then I have worked for major corporations like T-Mobile and Microsoft and many more, and I have founded a startup. I've also been teaching product management to ensure my students save time and money to get to the same level as me faster avoiding popular mistakes. Today as I’ve transitioned to teaching and focusing on the startup, I hear everybody being concerned about Ai stealing their jobs… We’ll talk about it shortly.
But before that, I want to take you back to 1997. One of my favorite movies is “Fifth Element”. It wowed me with futuristic predictions when I was a kid and I’m impressed by the number of these predictions that have already come true. Self-driving cars, video calls and smart TV, personalized ads and identity scanning. Sci-fi movies and books gave us many ideas and some are being implemented as we speak. But we often get ahead of ourselves:
Flying cars,Colonized planets, Human-like AI: not yet, Time travel, Mind-machine neural interfaces for everyone: Only in experimental stages (e.g. Neuralink).
Cyberpunk dystopias: Some vibes (neon signs + inequality + surveillance), but not total dystopia (thankfully).
On the bright side, we predict that the working hours should drop as Ai becomes our helper and there shouldn’t be a need to work 8 hours/day. Nobody knows for sure but we can require that from legislation. Instead of waiting to see what the government and billionaires come up with, I say we should design our own future.
So, we as humans, when we don’t know something - fear takes over. The same thing happened during the industrial revolution. In the Industrial Era, machines didn’t steal jobs—they transformed them but people were scared about their jobs. The AI era is making similar changes except it feels like robots will take the center stage instead of a human. First off, even when it comes to the hottest space in the military - drones, Ai does a fraction of work. AI algorithms enable real-time decision-making, obstacle avoidance, and mission optimization making drones far more autonomous and capable than traditional remote-controlled aircraft. Key technologies include computer vision for object detection, GPS-enhanced navigation, and neural networks for learning and adaptation. But guess what? There are only 2 companies right now that utilize Ai in drones to make autonomous decisions - Skydio and DJI.
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now
PyCon SG 25 - Firecracker Made Easy with Python.pdf
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Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical Universes
Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical UniversesSaikat Basu Embark on a cosmic journey exploring the intersection of quantum
computing, consciousness, and ancient wisdom. Together we'll uncover the
recursive patterns that bind our reality.
From Manual to Auto Searching- FME in the Driver's Seat
From Manual to Auto Searching- FME in the Driver's SeatSafe Software Finding a specific car online can be a time-consuming task, especially when checking multiple dealer websites. A few years ago, I faced this exact problem while searching for a particular vehicle in New Zealand. The local classified platform, Trade Me (similar to eBay), wasn’t yielding any results, so I expanded my search to second-hand dealer sites—only to realise that periodically checking each one was going to be tedious. That’s when I noticed something interesting: many of these websites used the same platform to manage their inventories. Recognising this, I reverse-engineered the platform’s structure and built an FME workspace that automated the search process for me. By integrating API calls and setting up periodic checks, I received real-time email alerts when matching cars were listed. In this presentation, I’ll walk through how I used FME to save hours of manual searching by creating a custom car-finding automation system. While FME can’t buy a car for you—yet—it can certainly help you find the one you’re after!
“MPU+: A Transformative Solution for Next-Gen AI at the Edge,” a Presentation...
“MPU+: A Transformative Solution for Next-Gen AI at the Edge,” a Presentation...Edge AI and Vision Alliance
You are not excused! How to avoid security blind spots on the way to production
You are not excused! How to avoid security blind spots on the way to productionMichele Leroux Bustamante
Ad
Machine Learning for Web Data
- 1. Machine Learning for Web Data Hilary Mason Web Directions USA 2010
- 3. = new capacities (superpowers) Machine learning is a way of thinking about data.
- 6. 6
- 7. wicked hard problem 10s of millions of URLs /day 100s of millions of events / day 1000s of millions of
- 11. @hmason
- 12. [archive photo]
- 13. ELIZA
- 17. ML Today
- 18. Algorithms + On-demand computing + Ubiquitous data
- 19. Algorithms New frames for modeling the world with data.
- 21. [moar data and new kinds of data]
- 22. Examples
- 23. [spam filters]
- 27. Machine Learning
- 30. Entity disambiguation This is important.
- 31. ME UGLY HAG
- 32. Entity disambiguation This is important. Company disambiguation is a very common problem – Are “Microsoft”, “Microsoft Corporation”, and “MS” the same company?
- 33. Classification
- 35. <math>
- 36. Probability P(A) is the probability that A is true.
- 37. Axioms of Probability 0 ≤ P(A) ≤ 1 P(True) = 1 P(False) = 0 P(A or B) = P(A) + P(B) – P(A and B)
- 38. P(A or B) = P(A) + P(B) – P(A and B) P(A) P(B) P(A and B)
- 39. Bayes Law
- 40. Example There are 10,000 people. 1% have a rare disease.
- 41. Example • Population of 10,000 • 1% have rare disease • There’s a test that is 99% effective. – 99% of sick patients test positive – 99% of healthy patients test negative
- 42. Given a positive test result, what is the probability that the patient is sick?
- 44. Disease Diagnosis 99 sick patients test positive, 99 healthy patients test positive Given a positive test, there is a 50% probability that the patient is sick.
- 45. Bayesian Disease Know the prob. of testing sick given healthy, and healthy given sick Use Bayes theorem to invert probabilities
- 46. </math>
- 49. 1. Obtain Data “pointing and clicking does not scale!” http://www.delicious.com/pskomoroch/dataset
- 53. 4. Model Google Prediction API http://code.google.com/apis/predict/
- 54. 4. Model Python • NLTK - http://www.nltk.org/ • Scikits Learn - http://scikit- learn.sourceforge.net/
- 56. 5. Interpret Andrew Vande Moore – Visual Poetry 06
- 58. One Final Example Twitter is full of noise. Sports – down Math – UP! Narcissism - down
- 59. Code!
- 60. Filtering & Relevance Ordering http://github.com/hmason/tc
- 61. What’s next?
- 62. Soon: Natural Language Generation Rich media classification Contextual everything
- 64. infer links in data
- 65. Filtering
- 66. Relevance