This post lists the challenges with getting an AI/ML project from development into production and offers suggestions on organizational and tooling changes (like KitOps' ModelKits) that can help. Tagged with devops, ai, opensource, aiops.

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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.

Why enterprise AI projects are moving too slowly

Jupyter notebooks are great...and terrible

Bridging the divide between data science and operations

<p>1 year on some startup === 3 years in corporation</p>
<p>in terms of self development.</p>
