karan842/mlops-best-practices
Practical guide to build end-to-end machine learning pipeline and deploy your model in production,
This guide helps data scientists and machine learning engineers create robust, end-to-end machine learning systems. It takes your raw data and trained models, and provides the best practices to turn them into reliable, scalable applications that can be deployed and monitored in a production environment. Anyone responsible for getting machine learning models from development to practical, real-world use will find this useful.
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Use this if you need a step-by-step roadmap to build, deploy, and manage machine learning models effectively in a production setting.
Not ideal if you are solely focused on the theoretical aspects of machine learning model development and not concerned with deployment or operational workflows.
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MIT
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Last pushed
Aug 28, 2023
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