Ultimate DevOps to MLOps Bootcamp Build ML CI/CD Pipelines

Ultimate DevOps to MLOps Bootcamp – Build ML CI/CD Pipelines
2025-08-11
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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

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

Description
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

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