Course Description

This course is designed to provide students with a comprehensive understanding of Machine Learning Operations (MLOps), focusing on practical skills and industry-standard tools. Students will learn to build and manage end-to-end ML pipelines, with a special emphasis on AWS technologies and GenAI applications.

Course Objectives

By the end of this course, students will be able to:

1. Understand the core principles and practices of MLOps
2. Implement ML pipelines using AWS services
3. Apply MLOps techniques to GenAI projects
4. Develop skills in data management, model training, and deployment
5. Gain hands-on experience with real-world MLOps scenarios

Course Modules

1.Version Control & CI/CD for ML

• Git Essentials and Workflow

• Data Version Control (DVC)

• Containerization with Docker

• Continuous Integration/Continuous Deployment (CI/CD) Pipelines

2. Model Tracking & Experimentation

• Experiment Tracking with MLflow

• Model Versioning and Management

• Hyperparameter Tuning Techniques

3. Model Deployment Strategies

• Containerizing ML Models

• Deploying Models on AWS EKS

• API Development with FastAPI and Flask

4. Monitoring & Observability

• Model Monitoring with Prometheus and Grafana

• Detecting Data Drift and Model Degradation

• Implementing Logging and Alerting Mechanisms

5. Cloud & DevOps Integration

• Utilizing AWS Services for MLOps

• Infrastructure as Code with CloudFormation

• Automating ML Pipelines with AWS Step Functions

6. Advanced Topics

• Feature Stores and Data Pipelines

• Model Governance and Compliance

• Ethical Considerations in MLOps