Program Objectives:

• Understand MLOps principles for deploying and managing machine learning models.
• Set up CI/CD pipelines for ML workflows.
• Implement model monitoring and retraining strategies.
• Gain hands-on experience with deployment automation tools.
• Explore tools for scaling ML pipelines in production environments.

What you will learn?

Module 1: Introduction to MLOps

• What is MLOps?
  • Definition and importance
  • Comparison with DevOps and DataOps
  • Benefits and challenges
• The MLOps Lifecycle
  • Model development
  • Deployment
  • Monitoring and maintenance
• MLOps Tools Overview
  • CI/CD for Machine Learning
  • Tools like MLflow, Kubeflow, and TensorFlow Extended (TFX)

Module 2: Fundamentals of Machine Learning

• Supervised, Unsupervised, and Reinforcement Learning
• Feature Engineering
  • Data preprocessing
  • Feature scaling and selection
• Model Training and Validation
  • Train-test split
  • Cross-validation
  • Hyperparameter tuning

Module 3: DevOps Essentials for MLOps

• Version Control Systems
  • Git and GitHub/GitLab
• CI/CD Pipelines
  • Continuous integration for ML
  • Continuous deployment for ML
• Infrastructure as Code (IaC)
  • Tools like Terraform and Ansible
• Docker and Containerization
  • Building and managing ML container images
• Kubernetes for Orchestration
  • Deploying ML models on Kubernetes

Module 4: Data Engineering in MLOps

• Data Pipelines
  • Building reproducible data pipelines
  • Tools like Apache Airflow and Prefect
• Data Storage and Versioning
  • Using DVC (Data Version Control)
  • Managing data lakes and warehouses
• Real-Time Data Processing
  • Kafka and Spark Streaming
• Data Security and Governance
  • Ensuring data privacy and compliance

Module 5: Model Deployment

• Deployment Strategies
  • Batch, online, and hybrid inference
  • A/B testing and canary deployments
• Serving Models in Production
  • Tools like TensorFlow Serving, FastAPI, and Flask
• Scaling and Load Balancing
  • Using Kubernetes and auto-scaling
• Edge Deployment
  • Deploying models on IoT and edge devices

Module 6: Monitoring and Maintenance

• Model Monitoring
  • Drift detection (data and model drift)
  • Performance metrics tracking
• Logging and Alerting
  • Using tools like Prometheus, Grafana, and ELK Stack
• Automated Retraining
  • Implementing feedback loops for continuous improvement
• Incident Response
  • Debugging and rollback strategies

Module 7: MLOps Tools in Practice

• Introduction to Key Tools
  • MLflow: Experiment tracking and model registry
  • Kubeflow: End-to-end pipeline orchestration
  • TFX: TensorFlow Extended ecosystem
• Hands-On Labs
  • Setting up and using MLOps pipelines with these tools

Module 8: Ethics and Governance in MLOps

• Responsible AI Practices
  • Fairness, accountability, and transparency
• Regulatory Compliance
  • GDPR, CCPA, and other regulations
• Auditability and Explainability
  • Building explainable and auditable ML systems

Module 9: Advanced Topics in MLOps

• Automated Machine Learning (AutoML)
  • Tools and frameworks like H2O.ai, Google AutoML
• Federated Learning
  • Concepts and applications
• Serverless MLOps
  • Serverless architectures for ML workflows
• MLOps in the Cloud
  • AWS, Azure, and GCP MLOps offerings

Module 10: Capstone Project

• End-to-End Implementation
  • Building a complete MLOps pipeline
  • Integration of CI/CD, data pipelines, and monitoring
• Evaluation and Presentation
  • Peer review and expert feedback