Build end-to-end ML pipelines with MLFLow, DVC, Docker, Flask, GitHub Actions, Chrome Plugging , and AWS
Welcome to the most hands-on and practical MLOps course designed for professionals looking to master real-world machine learning deployment.
What you’ll learn
- Build and deploy real-world machine learning models using MLOps Tools.
- Implement a complete Google Chrome Plugging.
- Implement a complete CI/CD pipeline for ML using GitHub Actions and model versioning.
- Track, manage, and compare ML experiments using DVC, MLflow for robust model governance.
- Design modular, reusable MLOps pipelines that follow industry best practices.
- Deploy and scale ML model on AWS cloud platforms with Docker production-ready architecture.
Course Content
- Introduction –> 1 lecture • 20min.
- Data Management & Preprocessing –> 2 lectures • 22min.
- Setting up MLFlow Server –> 1 lecture • 16min.
- Building Baseline Model with MLFlow –> 6 lectures • 38min.
- Building End to End Pipeline using DVC –> 6 lectures • 30min.
- Implementing Complete Chrome Plugin –> 2 lectures • 22min.
- CICD Deployment on AWS –> 2 lectures • 26min.
Requirements
Welcome to the most hands-on and practical MLOps course designed for professionals looking to master real-world machine learning deployment.
In this course, you won’t just learn theory — you’ll build and deploy production-grade ML pipelines using a modern stack including MLflow, DVC, Docker, Flask, GitHub Actions, and AWS. You’ll even integrate ML models into a Chrome plugin, showcasing end-to-end MLOps in action.
Projects You’ll Build:
– ML Sentiment Analyzer with MLflow & DVC
– Reproducible training pipeline with DVC + Git
– MLflow tracking dashboard with metrics & artifacts
– Dockerized inference service with REST API
– End-to-end CI/CD with GitHub Actions
– Live deployment on AWS EC2
– Chrome Extension that calls your ML API in real time
Why Take This Course?
- Get hands-on experience with modern MLOps tools
- Learn how to manage datasets, track models, and deploy to production
- Understand real-world DevOps practices applied to Machine Learning
- Build a portfolio of deployable, full-stack ML projects
- Gain job-ready skills for roles in MLOps, Data Engineering, and ML Engineering
Throughout this course, you’ll work on production-grade ML projects that simulate real business use cases, incorporating tools and frameworks of MLOps. Whether you’re looking to become an MLOps expert or deploy your first model professionally, this course equips you with the knowledge, code, and system design needed to succeed.