Complete MLOps Bootcamp With 10+ End To End ML Projects

Module 1: Introduction to MLOps & Setup

⏳ 30 minutes

• Overview of MLOps, CI/CD, and pipeline automation.

• Setting up Docker, Kubernetes, and Python environments.

Module 2: Version Control & Workflow Automation

⏳ 45 minutes

• GitHub Actions for ML automation and testing.

• Code and data versioning with DVC and Git.

Module 3: Experiment Tracking with MLflow

⏳ 60 minutes

• Logging parameters, metrics, and models using MLflow.

• Comparing model runs and storing artifacts.

Module 4: Model Building & Training Pipelines

⏳ 60 minutes

• Modularizing code with pipeline structure.

• Building, training, and evaluating ML models.

Module 5: API Development with FastAPI

⏳ 60 minutes

• Creating REST APIs for ML inference.

• Building interactive endpoints for prediction services.

Module 6: Dockerizing ML Applications

⏳ 45 minutes

• Containerizing ML apps using Docker.

• Creating reproducible environments and builds.

Module 7: Orchestrating Workflows with Kubeflow & Airflow

⏳ 75 minutes

• Building DAGs for ML workflows.

• Automating tasks like training, testing, and deployment.

Module 8: CI/CD Pipelines for ML

⏳ 60 minutes

• Automating model testing, packaging, and deployment.

• Integrating GitHub Actions and Docker into CI/CD.

Module 9: Deployment to Cloud & Kubernetes

⏳ 60 minutes

• Deploying ML models using Kubernetes and FastAPI.

• Scaling and updating models in production environments.

Module 10: Monitoring & Model Drift Detection

⏳ 60 minutes

• Setting up monitoring dashboards and alerts.

• Detecting model drift and triggering retraining.

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