MLOps Fundamentals – Learn MLOps Concepts with Azure demo

Module 1: Introduction to MLOps

⏳ 30 minutes

• What is MLOps and why it’s critical in modern ML systems.

• Key challenges in deploying and managing ML models.

Module 2: ML Lifecycle & Pipeline Structure

⏳ 45 minutes

• Understanding stages: development, training, validation, deployment, and monitoring.

• Building scalable and repeatable pipelines.

Module 3: Version Control with Git & DVC

⏳ 60 minutes

• Tracking code and dataset versions for reproducibility.

• Using Git and DVC for collaborative ML development.

Module 4: MLflow for Experiment Tracking

⏳ 60 minutes

• Logging experiments, models, and metrics with MLflow.

• Model registry, tracking server, and reproducible pipelines.

Module 5: Containerization with Docker

⏳ 45 minutes

• Creating containerized environments for ML projects.

• Building portable and consistent deployment setups.

Module 6: CI/CD Pipelines for ML Projects

⏳ 60 minutes

• Automating training, testing, and deployment steps.

• Tools like GitHub Actions and Jenkins in ML workflows.

Module 7: Orchestration with Airflow/Kubeflow

⏳ 60 minutes

• Managing end-to-end workflows for model training and deployment.

• Scheduling, monitoring, and retry mechanisms.

Module 8: Model Serving & Monitoring

⏳ 60 minutes

• Deployment strategies: batch, real-time, and A/B testing.

• Monitoring model performance, drift, and feedback loops.

Module 9: Real-World Project: End-to-End MLOps

⏳ 75 minutes

• Implementing a complete MLOps project pipeline from data to deployment.

• Best practices and lessons learned.

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