chansonZ/book-ml-sem
《机器学习：软件工程方法与实现》Method and implementation of machine learning software engineering
This resource helps experienced machine learning practitioners build robust, production-ready ML systems. It guides you through the entire lifecycle, from preparing data and engineering features to tuning models and deploying them reliably. You'll learn to apply software engineering principles to your ML projects, going beyond basic model training to create stable and scalable solutions.
186 stars. No commits in the last 6 months.
Use this if you are an advanced machine learning practitioner looking to deepen your understanding of software engineering best practices for deploying and managing ML models in real-world scenarios.
Not ideal if you are a beginner looking for an introduction to the basic concepts of machine learning or if you primarily focus on competitive modeling without a strong need for production deployment.
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Jupyter Notebook
License
MIT
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Last pushed
Feb 02, 2023
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