Training on Cloud AI and MLOps: Deploying and Managing Models

Module 1: Introduction to MLOps and Cloud AI

• Understanding MLOps and its importance
• Cloud platforms for AI: AWS, Azure, GCP
• MLOps lifecycle and key components
• Core MLOps principles and practices
• Building a business case for MLOps
• Case Study: Analyzing MLOps implementation

Module 2: Building MLOps Pipelines

• Designing MLOps pipelines
• Data ingestion and preprocessing in pipelines
• Model training and validation workflows
• Model evaluation and selection
• Model registry and versioning
• Case Study: Building an MLOps pipeline on a cloud platform

Module 3: Deploying Models to Production

• Model deployment strategies: batch, real-time, edge
• Containerization and orchestration
• Model serving and API endpoints
• Monitoring model performance and drift
• Managing model scaling and updates
• Case Study: Deploying a model to production

Module 4: Automating ML Workflows

• CI/CD for ML: integration and delivery
• Automated testing and validation of ML models
• Workflow automation with pipelines
• Managing dependencies and environments
• Continuous monitoring and retraining
• Case Study: Automating an ML workflow

Module 5: Advanced MLOps Topics

• Feature stores and feature engineering pipelines
• Model governance and compliance
• Managing multi-model and multi-team environments
• Cost optimization in cloud AI
• Emerging MLOps trends and tools
• Case Study: Implementing advanced MLOps practices

Module 6: Model Monitoring and Performance Management

• Monitoring model performance and quality
• Detecting and managing model drift
• Model retraining and updating strategies
• Alerting and incident management for models
• Visualizing model performance metrics
• Case Study: Building a model monitoring and alerting system

Module 7: Infrastructure as Code for AI

• Infrastructure as Code (IaC) principles
• Managing cloud resources for AI workloads
• Automating infrastructure provisioning with Terraform
• Managing configuration and dependencies
• Ensuring reproducibility and consistency
• Case Study: Implementing IaC for AI infrastructure

Module 8: MLOps Security and Compliance

• Securing ML pipelines and infrastructure
• Managing access control and authentication
• Ensuring data privacy and compliance
• Model security and adversarial threats
• Auditing and logging for MLOps
• Case Study: Implementing security and compliance in MLOps

Module 9: MLOps for Generative AI

• Deploying large language models (LLMs) and generative AI
• Managing generative AI models in production
• Scaling and optimizing LLM inference
• Prompt engineering in production
• Monitoring generative AI models
• Case Study: Deploying a generative AI model

Module 10: Advanced MLOps and Future Trends

• MLOps for multi-cloud environments
• Serverless and managed ML services
• Automated machine learning (AutoML) in MLOps
• Responsible MLOps and ethical AI practices
• Future trends and directions in MLOps
• Case Study: Designing an advanced MLOps architecture