santiagxf/mlflow-deployments
Source code for the post Effortless deployments with MLFlow, showcasing how logging models using MLFLow can provide you want to easily deploy them in production later.
This project helps machine learning engineers and data scientists take their trained models, from computer vision to recommendation systems, and put them into action. It demonstrates how to package models, customize how they make predictions, and deploy them to various environments like local machines, Kubernetes, or cloud platforms. The result is a ready-to-use prediction service.
No commits in the last 6 months.
Use this if you are a machine learning engineer or data scientist looking to streamline the process of getting your trained models from experimentation to production.
Not ideal if you are looking for a platform that automatically trains models for you or if you are not comfortable working with machine learning model deployment concepts.
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Last pushed
Jul 08, 2023
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