Update content of Data Science tab #272
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That's nice, more info but doesn't seem dense. How about this for the second paragraph: For high data volumes, Dask and Ray are designed to scale. Stable production environments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow and Prefect). |
Follow up to numpygh-242, alternative to numpygh-262. The content rewrite and inclusion of more relevant libraries attempts to make this sound natural, sketch the breadth of the Python data science offerings, and keeps some of the tools like DVC and MLFlow that beginning to intermediate data scientists really need to learn about. It does shrink the amount of content to a more reasonable size.
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Thanks @bjnath, that phrasing sounds better than mine, I've taken it over. |
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In it goes |
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Do you like "stable production deployments rely on" ( or "stable production deployment relies on")? |
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I'm happy with that. |
rgommers
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May 24, 2020
Follow up to numpygh-272 and numpygh-258
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Follow up to gh-242, alternative to gh-262. The content rewrite and inclusion of more relevant libraries attempts to make this sound natural, sketch the breadth of the Python data science offerings,
and keeps some of the tools like DVC and MLFlow that beginning to intermediate data scientists really need to learn about.
It does shrink the amount of content to a more reasonable size: