[100% Off] Ultimate Devops To Mlops Bootcamp - Build Ml Ci/Cd Pipelines
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
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.
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.
