MLOps Tutorial: Build a Full ML Pipeline with MLflow, DVC & Deploy on AWS

🚀 Go from building baseline models to deploying a complete, production-ready ML pipeline on AWS! This course teaches you the essential MLOps tools and techniques for creating reproducible, scalable, and collaborative machine learning projects. In this comprehensive guide, you'll master the entire MLOps workflow. We'll start with experiment tracking using MLflow, build a version-controlled pipeline with DVC, and finally, deploy our application using Docker and a full CI/CD pipeline on AWS. This is the practical, hands-on experience you need to level up your ML engineering skills. What You Will Master in This Course: Design & Build ML Pipelines: Create robust, reproducible ML workflows using MLflow for experiment tracking and DVC for data/model versioning. Optimize ML Models: Go beyond the basics. Learn to improve model performance with techniques like BOW, TF-IDF, hyperparameter tuning, and model stacking. Deploy ML Projects on AWS: Master the art of production ML. Use DVC, Docker, and CI/CD to build and deploy end-to-end pipelines at scale. Build a Real Application: Integrate your deployed model with a custom Google Chrome plugin for a true end-to-end project experience. 🛠️ Key Tools & Technologies Covered: MLflow, DVC (Data Version Control), Python, Scikit-learn, Docker, AWS (Amazon Web Services), Git, CI/CD, BOW, TF-IDF. Timestamp: 00:00 - Project Planning & Introduction (Part 1) 01:29 - Free Courses 01:45 - Project Planning & Introduction (Part 2) 15:13 - Data Collection 16:47 - Data Preprocessing & EDA 35:56 - Setup MLFlow Server on AWS 48:19 - Building Baseline Model 56:13 - Improving Baseline Model - BOW, TF-IDF 1:03:32 - Improving Baseline Model - Max features 1:08:33 - Improving Baseline Model - Handling Imbalanced Data 1:13:33 - Improving Baseline Model - Hyperparameter tuning with Multiple Models 1:18:31 - Improving Baseline Model - Stacking Models 1:20:30 - Building an ML Pipeline using DVC 1:20:30 - Data Ingestion Component 1:30:37 - Data Preprocessing Component 1:33:06 - Model Building Component 1:36:49 - Model Evaluation Component with MLFlow 1:43:56 - Model Register Component with MLFlow 1:46:29 - Flask API Implementation 1:56:56 - Implementation of Chrome Plugin 2:05:58 - Adding Docker 2:07:11 - Deployment on AWS