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
Requirements
- Basic knowledge of DevOps and Docker
- Familiarity with Git and GitHub
- Some exposure to Python (used for scripting and ML workflows)
- Prior understanding of CI/CD concepts is helpful but not mandatory
- A machine with minimum 8GB RAM and Docker installed for running local labs
Who This Course is For
- DevOps Engineers looking to break into the field of MLOps
- Platform Engineers and SREs supporting ML teams
- Cloud Engineers wanting to understand ML workflows and productionization
- Developers transitioning into ML Engineering or Data Engineering roles
- Anyone curious about how real-world ML systems are deployed and scaled
Your Instructor
Gourav J. Shah
Premium Instructor | 200,000+ Students | DevOps/AIOps/MLOps
4.4 Instructor Rating
4,990 Reviews
197,477 Students
27 Courses
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