Day 1: Introduction to MLOps and ML Workflow Automation

Session 1: Understanding MLOps in AI and Data Science

• What is MLOps? Key concepts and principles
• The ML lifecycle: from data preparation to model deployment
• Benefits of MLOps for AI scalability, automation, and governance

Session 2: MLOps Architecture and Components

• Data versioning, model tracking, and deployment pipelines
• Introduction to MLOps tools: MLflow, Docker, Kubernetes, Jenkins
• Real-world examples of MLOps in enterprise AI environments

Session 3: Hands-on Lab: Setting Up an MLOps Environment

• Installing and configuring MLflow for model tracking
• Introduction to Docker for containerizing ML models
• Setting up Git for version control and collaborative ML workflows

Session 4: Automating ML Workflows with CI/CD Pipelines

• Understanding CI/CD concepts for machine learning
• Building CI/CD pipelines for continuous model integration and delivery
• Automating data preprocessing, model training, and deployment

Session 5: Hands-on Lab: Implementing a CI/CD Pipeline for ML Models

• Creating a CI/CD pipeline using GitHub Actions or Jenkins
• Automating model testing, training, and deployment workflows
• Deploying a simple ML model with automated version control

Day 2: Model Deployment, Monitoring, and Management

Session 1: Model Deployment Strategies in MLOps

• On-premise vs. cloud-based deployment
• Introduction to model serving with RESTful APIs
• Deploying models using Docker containers

Session 2: Hands-on Lab: Deploying AI Models with Docker

• Containerizing a machine learning model using Docker
• Creating RESTful APIs for model serving with FastAPI or Flask
• Deploying models locally and testing API endpoints

Session 3: Scaling AI Models with Kubernetes

• What is Kubernetes? Introduction to container orchestration
• Deploying machine learning models in Kubernetes clusters
• Managing model scaling, load balancing, and resource optimization

Session 4: Hands-on Lab: Deploying ML Models on Kubernetes

• Setting up Kubernetes clusters for AI model deployment
• Deploying and managing containerized ML applications
• Monitoring resource usage and scaling models dynamically

Session 5: Model Monitoring and Performance Management

• Importance of model monitoring in production environments
• Tracking model performance metrics: accuracy, latency, throughput
• Detecting model drift and triggering retraining pipelines

Session 6: Hands-on Lab: Model Monitoring with MLflow

• Using MLflow to monitor deployed models in real-time
• Setting up alerts for performance degradation
• Implementing automated retraining triggers for model drift

Day 3: Advanced MLOps Practices and Capstone Project

Session 1: Advanced MLOps Techniques

• Feature stores for managing training and inference data
• A/B testing and model experimentation in production
• Securing ML pipelines: data privacy, compliance, and governance

Session 2: Real-World Case Studies in MLOps

• Case study 1: Deploying an AI-powered recommendation system with MLOps
• Case study 2: Automating fraud detection models in financial services
• Case study 3: Scaling AI models for real-time analytics in e-commerce

Session 3: Capstone Project: Automating an End-to-End ML Workflow

• Group project: Design, develop, and deploy an AI model with MLOps automation
• Applying CI/CD, containerization, deployment, and monitoring techniques
• Presenting project outcomes, workflows, and performance metrics

Session 4: Key Takeaways and MLOps Best Practices

• Best practices for successful MLOps implementation in organizations
• Challenges in MLOps adoption and strategies to overcome them
• Future trends in MLOps: AutoML, edge deployment, and AI observability

Session 5: Final Q&A and Course Wrap-Up

• Open Q&A session to address participants’ specific questions
• Course feedback and discussion of next steps for advanced MLOps learning