AI/ML projects face challenges in coordination, collaboration, and deployment due to the different assets involved. Jupyter notebooks are useful for data scientists in experimentation, but not easily extractable for other teams. Model development for production requires additional considerations and operational needs. Integrating data science and operations teams can help bridge the gap and enhance workflow efficiency. AI projects in production require proper packaging and versioning. Jozu's KitOps offers a solution for packaging and versioning AI projects into ModelKits.
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AI/ML isn’t just about the codeJupyter notebooks are great...and terribleWhat about containers?Bridging the divide between data science and operationsThere’s more...1 Comment
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