Machine Learning Deployment - A Product Recommendation Engine Deployed and Tracked with a Local Machine Learning - Workbench
Posted on Tue 15 February 2022 in Machine Learning Deployment & Tracking
Train & Serve a Machine Learning Model as a Microservice using Docker and Track the Model-Performance using MLflow
Let's build a recommendation engine with a collaborative filtering model, track the model results with MLflow and use Flask to serve batch-predictions of related products other users may like as well
Background
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