mtpatter/mlflow-tutorial
Fully reproducible, Dockerized, step-by-step, tutorial on training and serving a simple sklearn classifier model using mlflow. Detailed blog post published on Towards Data Science.
This project helps machine learning engineers and data scientists deploy trained models into a live environment where they can be used to make predictions. You'll input a trained scikit-learn classification model, and the output will be a running web service that can receive new data and return predictions. This is for professionals who need to move their models from development to practical application.
No commits in the last 6 months.
Use this if you need to understand the fundamental steps of taking a trained scikit-learn model and exposing it as an API endpoint for others to consume, while also learning about model tracking and versioning.
Not ideal if you are looking for advanced MLOps strategies, highly scalable production deployments, or solutions beyond basic model serving.
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Sep 20, 2024
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