From Data to Deployment — Learn MLOps by Building a Real-World Machine Learning Project with MLflow, Docker, Kubernetes
What you'll learn
- 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