This hands-on bootcamp is designed to help DevOps Engineers and infrastructure professionals transition into the growing field of MLOps. With AI/ML rapidly becoming an integral part of modern applications, MLOps has emerged as the critical bridge between machine learning models and production systems.

In this course, you will work on a real-world regression use case — predicting house prices — and take it all the way from data processing to production deployment on Kubernetes. You’ll start by setting up your environment using Docker and MLFlow for tracking experiments. You’ll understand the machine learning lifecycle and get hands-on experience with data engineering, feature engineering, and model experimentation using Jupyter notebooks.

Next, you'll package the model with FastAPI and deploy it alongside a Streamlit-based UI. You’ll write GitHub Actions workflows to automate your ML pipeline for CI and use DockerHub to push your model containers.

In the later stages, you'll build a scalable inference infrastructure using Kubernetes, expose services, and connect frontends and backends using service discovery. You’ll explore production-grade model serving with Seldon Core and monitor your deployments with Prometheus and Grafana dashboards.

Finally, you'll explore GitOps-based continuous delivery using ArgoCD to manage and deploy changes to your Kubernetes cluster in a clean and automated way.

By the end of this course, you'll be equipped with the knowledge and hands-on experience to operate and automate machine learning workflows using DevOps practices — making you job-ready for MLOps and AI Platform Engineering roles.