Learn C# Programming Polymorphism & Operator Overloading
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This document discusses polymorphism and operator overloading in C#. It defines polymorphism as having many forms, and describes static and dynamic polymorphism. Static polymorphism uses function and operator overloading at compile time, while dynamic polymorphism uses abstract classes and virtual functions to determine behavior at runtime. Function overloading allows multiple definitions of the same function name, differing by type or number of arguments. Operator overloading allows redefining operators for user-defined types. The document provides examples of function overloading, dynamic polymorphism using abstract classes and virtual functions, and operator overloading.
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Monitoring ModelsEng Teong Cheah The document discusses the importance of monitoring Azure Machine Learning resources for availability, performance, and operation using Azure Monitor. It describes the types of monitoring data generated and how to analyze it, including metrics and logs collection methods. Additionally, it highlights the significance of transparency and interpretability in AI systems for decision-making processes.
Responsible Machine Learning
Responsible Machine LearningEng Teong Cheah Responsible AI refers to developing and deploying AI systems in a safe, ethical, and trustworthy manner, centered around principles like fairness, reliability, privacy, inclusiveness, transparency, and accountability. Microsoft has established a Responsible AI standard to guide these efforts, employing tools such as the Responsible AI dashboard in Azure Machine Learning to assess fairness, interpretability, and privacy while ensuring robust accountability through MLOps practices. As AI technology becomes ingrained in daily life, prioritizing these principles is essential for fostering trust and achieving equitable outcomes.
Training Optimal Models
Training Optimal ModelsEng Teong Cheah The document discusses hyperparameter tuning in machine learning, specifically using Azure Machine Learning to optimize model performance through multiple training runs with varying hyperparameters. It explains defining search spaces for discrete and continuous hyperparameters, and emphasizes the importance of configuring sampling and early termination policies to improve efficiency during the tuning process. It also provides links and references for further information.
Deploying Models
Deploying ModelsEng Teong Cheah The document discusses machine learning inferencing, outlining the distinction between batch and real-time inference methods. Batch inference processes predictions in scheduled intervals, storing results for later use, while real-time inference offers immediate predictions for applications needing rapid responses. Best practices for implementing both methods in Azure Machine Learning, including compute options and deployment strategies, are provided.
Machine Learning Workflows
Machine Learning WorkflowsEng Teong Cheah The document discusses machine learning workflows, particularly focusing on the concept of pipelines as executable processes. It covers what pipeline steps are and how to create a pipeline. Additional resources and references to Microsoft documentation are provided.
Working with Compute
Working with ComputeEng Teong Cheah The document discusses using compute options for experiment runs, focusing on local compute environments such as development workstations or Azure Machine Learning compute instances. It highlights the importance of understanding compute clusters and references Microsoft documentation for further guidance. The content is related to the collaboration with Teong Cheah, a Microsoft MVP.
Working with Data
Working with DataEng Teong Cheah The document discusses the use of datastores in Azure Machine Learning, which serve as abstractions for connecting to cloud data sources. It details the types of datastores supported, including Azure Storage and SQL Database, as well as the concept of datasets, which are versioned data objects used for experiments and pipelines. Different types of datasets are described, focusing on tabular and file datasets for structured and unstructured data, respectively.
Experiments & TrainingModels
Experiments & TrainingModelsEng Teong Cheah Experiments involve grouping multiple runs from a script and are associated with a workspace. Each run requires an experiment name, and if the name is new, an experiment is created automatically. The document also references demo runs and training models.
Automated Machine Learning
Automated Machine LearningEng Teong Cheah Automated machine learning in Azure allows users to train models without extensive data science knowledge. It automatically tries different preprocessing techniques and algorithms in parallel to find the best performing model. Users can create an automated machine learning job that configures settings like the training script and compute target before starting a run. The automated process prepares data, trains and evaluates multiple models, and can deploy the best performing model as a predictive service.
Getting Started with Azure Machine Learning
Getting Started with Azure Machine LearningEng Teong Cheah Azure Machine Learning is a cloud service designed for managing the machine learning project lifecycle, allowing professionals to train, deploy, and manage models effectively. It is particularly beneficial for data scientists, ML engineers, and application developers, offering tools for model integration and a secure production environment. The workspace feature centralizes resources and maintains a history of jobs, facilitating easier workflows and collaboration.
Hacking Containers - Container Storage
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Hacking Containers - Looking at Cgroups
Hacking Containers - Looking at CgroupsEng Teong Cheah This document discusses containers and control groups (cgroups) in Linux. It explains how to create a simple Docker container to explore cgroups. The container is built using a Dockerfile and run in interactive mode. When running, the process ID of the container can be found in the proc directory and its cgroup location output. The document also discusses namespaces and how they work with cgroups to constrain processes and kernel objects. Namespaces limit what a process can see using APIs like clone(), setns(), and unshare(). Exploits may need to be rewritten to work across namespaces.
Hacking Containers - Linux Containers
Hacking Containers - Linux ContainersEng Teong Cheah This document discusses Linux containers and provides an overview of their internals. It notes that containers were not originally based on any standard but that the Open Container Initiative (OCI) later established standards. It describes key components of containers like OCI-compliant images and runtimes, chroot technology, and union mount file systems. It also discusses cgroups and how they were introduced in Linux kernels starting in version 2.6.24 to provide kernel-level resource controls for processes like CPU and disk limits.
Data Security - Storage Security
Data Security - Storage SecurityEng Teong Cheah The document discusses data security and storage practices in Microsoft Azure, with a focus on data sovereignty and the implications of storing information based on geographical laws. It outlines Azure storage access methods, including shared access signatures and Azure Active Directory authentication, emphasizing best practices for security, such as limiting role scopes and ensuring secure connections. Additionally, it addresses blob data retention policies and the importance of identity-based authentication in managing Azure files.
Application Security- App security
Application Security- App securityEng Teong Cheah The document discusses application security and the Microsoft Identity Platform, highlighting OAuth 2.0 and OpenID Connect for authentication. It details the app registration process in Azure AD, necessary permissions, and requirements for using web app certificates. Additionally, it provides resources and contact information for further inquiries.
Application Security - Key Vault
Application Security - Key VaultEng Teong Cheah Azure Key Vault helps solve problems with secrets management, key management, and certificate management. It manages X509 certificates, supports RSA and elliptic curve keys, and allows storage of name-value pair secrets. Keys and secrets can be rotated without affecting applications by manual, programmatic, or automated processes.
Compute Security - Container Security
Compute Security - Container SecurityEng Teong Cheah The document discusses container security focusing on Azure services like Azure Container Instances (ACI) and Azure Container Registry (ACR), detailing best practices for securing containers, such as using approved images and role-based access. It further elaborates on Azure Kubernetes Service (AKS) as a platform for managing containerized workloads, featuring automation and scaling capabilities. Key components and terminology related to ACI, ACR, and AKS, as well as their security features, are also outlined.
Compute Security - Host Security
Compute Security - Host SecurityEng Teong Cheah This document discusses various compute security topics for hardening endpoints and hosts, including using Azure Security Center to protect endpoints from attacks, implementing privileged access workstations, creating virtual machine templates to improve consistency and security, and how Security Center provides recommendations for security settings, updates, and threat detection. It also mentions demonstrating Azure Firewall and provides a reference link for further information.
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- 1. Learn C# Programming Polymorphism & Operator Overloading Eng Teong Cheah MVP Visual Studio & Development Technologies
- 2. Polymorphism
- 3. Polymorphism The word polymorphism means having many forms. In object-oriented programming paradigm, polymorphism is often expressed as ‘one interface, multiple functions’. Polymorphism can be static or dynamic. In static polymorphism, the response to a function is determined at the compile time. In dynamic polymorphism, it is decided at runtime.
- 4. Static Polymorphism The mechanism of linking a function with an object during compile time is called early binding. It is also called static binding. C# provides two techniques to implement static polymorphism. They are: - Function overloading - Operator overloading
- 5. Function Overloading You can have multiple definitions for the same function name in the same scope. The definition of the function must differ from each other by the types and/or the number of arguments in the argument list. You cannot overload function declarations that differ only by return type.
- 7. Dynamic Polymorphism C# allows you to create abstract classes that are used to provide partial class implementation of an interface. Implementation is completed when a derived class inherits from it. Abstract classes contain abstract methods, which is implemented by the derived class. The derived classes have more specialized functionality.
- 8. Dynamic Polymorphism Here are the rules about abstract classes- - You cannot create an instance of an abstract class - You cannot declare an abstract method outside an abstract class - When a class is declared sealed. It cannot be inherited, abstract classes cannot be declared sealed.
- 10. Dynamic Polymorphism When you have a function defined in a class that you want to be implemented in an inherited class(es), you use virtual functions. The virtual functions could be implemented differently in different inherited class and the call to these functions will be decided at runtime. Dynamic polymorphism is implemented by abstract classes and virtual functions.
- 11. Demo 2 – Dynamic Polymorphism
- 13. Operator Overloading You can redefine or overload most of the built-in operators available in C#. Thus programmer can use operators with user defined types as well. Overloaded operators followed by the symbol for the operator being defined. Similar to any other function, an overload operator has a return type and parameter list.
- 14. Operator Overloading For example, go through the following function – The above function implements the addition (+) for a user defined class Box. It adds the attributes of two Box objects and returns the resultant Box object.
- 15. Demo – Implementing the Operator Overloading
- 16. Overloadable and Non-Overloadable Operators The following table describes the overload ability of the operators in C#
- 17. Demo – Overloadable & Non-Overloadable Operators
- 19. Thank you