Machine Learning and MLOps on Azure with Azure ML

Software Requirements

• Microsoft Azure Account
• Azure Machine Learning SDK
• Python
• Azure ML Studio
• Visual Studio Code or Jupyter Notebooks
• Access to Azure DevOps or GitHub

Introduction to Azure Machine Learning

• Overview of Azure Machine Learning services
• Key components: compute, storage, datasets, and environments
• Navigating Azure ML Studio and setting up a workspace
• Understanding the Azure ML SDK and CLI

Data Preparation and Feature Engineering

• Ingesting data from Azure Blob Storage, SQL databases, and Data Lake
• Transforming and cleaning data using Azure ML Designer
• Handling missing values, outliers, and categorical encoding
• Applying feature scaling and selection techniques
• Implementing effective data splitting strategies (train/test/validation)

AutoML

• Introduction to AutoML in Azure ML Studio
• Configuring AutoML for classification, regression, and time-series forecasting
• Comparing AutoML-generated models with manually trained models
• Interpreting AutoML results to select the best model
• Leveraging ensemble techniques within AutoML

Azure ML Designer (Graphical Approach)

• Introduction to the drag-and-drop interface of Azure ML Designer
• Building pipelines using graphical components
• Data ingestion (Import Data, Create Dataset)
• Data transformation (Clean Missing Data, Normalize Data, Select Columns)
• Feature engineering (Principal Component Analysis, Feature Selection)
• Model training (Train Model, Tune Model Hyperparameters)
• Evaluation (Evaluate Model, Cross-Validate Model)
• Deployment (Score Model, Web Service Output)
• Managing and versioning pipelines created in Azure ML Designer

Coded ML using Jupyter Notebooks

• Setting up Jupyter Notebooks in the Azure ML environment
• Running experiments and tracking results using the Azure ML SDK
• Programmatically managing datasets and compute clusters
• Leveraging notebooks for exploratory data analysis and custom model development

Building and Managing ML Pipelines

• Understanding the components of ML pipelines
• Integrating steps for data ingestion, transformation, training, and evaluation
• Scheduling and automating pipeline execution
• Utilizing pipeline caching and reusability for improved efficiency

Introduction to MLOps

• Defining MLOps and its significance in modern ML deployments
• Overview of MLOps capabilities in Azure
• Managing model registries and assets
• Integrating Git for version control and collaboration

Establishing an MLOps Environment in Azure

• Configuring an Azure ML workspace for MLOps
• Overview of Azure DevOps (repositories, pipelines, artifacts)
• Integrating Azure ML with Azure DevOps
• Automating workflows using GitHub Actions
• Incorporating LLMOps with Prompt Flow and GitHub for large language model applications

Model Development, Versioning, and Deployment

• Training and tracking ML models for production readiness
• Versioning models using built-in tools and registries
• Containerizing models using Docker and Azure Container Registry
• Orchestrating deployments with AKS, ACI, and Azure Functions
• Implementing CI/CD pipelines for seamless, automated deployment

Monitoring and Managing Deployed Models

• Collecting model performance and usage data
• Detecting data drift and performance degradation
• Automating model retraining workflows
• Managing the model lifecycle in a production environment

Scaling and Optimization

• Designing scalable and robust ML pipelines
• Implementing autoscaling and distributed training strategies
• Leveraging parallelism for large-scale ML workloads
• Advanced hyperparameter tuning for performance gains
• Model pruning and inference optimization techniques
• Cost optimization strategies in Azure ML

Continuous Improvement and Feedback Loop

• Collecting real-world feedback from deployed models
• Analyzing model performance data for continuous improvement
• Iterative development and versioning best practices
• Conducting A/B testing and controlled experiments
• Establishing continuous learning cycles for ongoing optimization