Best Practices For Sharing Your Work As A Machine Learning Engineer
Best Practices For Sharing Your Work As A Machine Learning Engineer Daniel Bourke July 17th, 2024 16 min read
The AI repository best practices framework provides a structured approach to organizing and documenting code repositories for AI and machine learning projects. It establishes clear standards across five critical categories, with tiered implementation levels to accommodate different project stages and requirements.
Engineering best practices for Machine Learning
Engineering best practices for Machine Learning The list below gathers a set of engineering best practices for developing software systems with machine learning (ML) components. These practices were identified by engaging with ML engineering teams and reviewing relevant academic and grey literature.
Machine learning projects necessitate diverse teams with specialized roles like ML product managers, data scientists, and ML engineers , to address various aspects of development and deployment. Comprehensive process documentation is crucial for ML teams to establish common practices , ensure smooth collaboration, and enhance project velocity by reducing confusion and streamlining workflows ...
Leveling Up as an ML engineer
Machine Learning (ML) engineering is more than data manipulation and model training — it requires a versatile skill set that extends beyond data science into software engineering.
ML Universal Guides
Rules of ML Become a better machine learning engineer by following these machine learning best practices used at Google.
Learn tips and best practices for making your machine learning code and data accessible, reproducible, and understandable by others.
3. Model Sharing Increase citations, ease review & collaboration ...
Learn how to share your machine learning projects with your peers in a clear, engaging, and ethical way. Get tips on choosing the platform, documenting the code, visualizing the data, sharing the ...
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