There’s no single data science project documentation recipe. Rather, your documentation needs will vary by project, team, organization, and industry. And it’s not just about producing data science model documentation. Instead, think broader and ask – What do I need to document and why? Once you’ve thought this through and […]
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Machine Learning Model Operations is a multi-disciplinary field that is gaining traction as organizations are realizing that there’s a lot more work even after model deployment. Rather, the model maintenance work often requires more effort than the development and deployment of a model. Hence, the world of Machine Learning Operations […]
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Machine Learning Project Management is of growing importance If you are leading machine learning projects, you have probably noticed (or soon will notice) that machine learning project management is of growing importance and that it would be extremely helpful to have a repeatable agile process framework that helps to ensure […]
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Data Science vs Software Engineering isn’t really a battle. Rather these are two somewhat overlapping and complementary fields that are similar in many ways. Data science intersects various domains However, dig deeper in the discussion of data science vs software engineering, and you’ll find key differences in the two fields: […]
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One of the most common questions of data science project management is some variation of: “Is agile a fit for data science?” Unfortunately – like a lot of questions in data science – this question itself is often misunderstood. Many (if not most) blog posts, on-line forums, and conversations debate […]
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Data science projects are unique. It’s time to start managing them as such.
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