Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn to build a complete MLOps pipeline that analyzes YouTube sentiment in real-time through a Chrome extension in this comprehensive 2 hour 51 minute course. Master the entire machine learning workflow from data collection and preprocessing to model deployment using modern MLOps tools including MLflow for experiment tracking, DVC for pipeline management, Docker for containerization, and AWS for cloud deployment. Start with project planning and data collection, then dive into exploratory data analysis and preprocessing techniques. Set up an MLflow server on AWS to track your experiments as you build and iteratively improve baseline models using techniques like Bag of Words, TF-IDF, feature engineering, imbalanced data handling, and hyperparameter tuning. Explore advanced modeling approaches including model stacking before constructing a robust ML pipeline with DVC that includes data ingestion, preprocessing, model building, evaluation, and registration components. Implement a Flask API to serve your model predictions and develop a Chrome extension that provides real-time sentiment analysis of YouTube content. Complete the project by containerizing your application with Docker and implementing CI/CD deployment on AWS for a production-ready solution. Gain hands-on experience with real-world ML engineering practices and modern MLOps workflows that are essential for deploying machine learning systems at scale.
