Ultimate DevOps to MLOps Bootcamp – Build ML CI/CD Pipelines

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From Data to Deployment β€” Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes

What you will learn


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Build end-to-end Machine Learning pipelines with MLOps best practices

Understand and implement ML lifecycle from data engineering to model deployment

Set up MLFlow for experiment tracking and model versioning

Package and serve models using FastAPI and Docker

Automate workflows using GitHub Actions for CI pipelines

Deploy inference infrastructure on Kubernetes using KIND

Use Streamlit for building lightweight ML web interfaces

Learn GitOps-based CD pipelines using ArgoCD

Serve models in production using Seldon Core

Monitor models with Prometheus and Grafana for production insights

Understand handoff workflows between Data Science, ML Engineering, and DevOps

Build foundational skills to transition from DevOps to MLOps roles

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