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Model versioning

Robust MLOps with Open-Source: ModelDB, Docker, Jenkins and Prometheus

A few weeks back, I had the pleasure of giving one of the four talks on the ML in Product Talk at the Strata OReilly Superstream. Since the Strata in-person conference got canceled, the fantastic folks at OReilly organized an extremely well-attended…

Model Versioning Done Right: Making your Scikit-learn models reproducible with ModelDB 2.0

At Verta, we ran our first ModelDB 2.0 webinar last week and it was a lot of fun. This blog post is a recap of the hands-on tutorial part of the webinar. For the full webinar content, check out the webinar recording on the Verta Youtube channel and the…

ModelDB 2.0 is here!

Since we wrote ModelDB 1.0, a pioneering model versioning system, we have learned a lot and adapting it to the evolving ecosystem became a challenge. Hence we decided to rebuild from the ground up to support a model versioning system tailored to make ML…

LeadCrunch-case-study

Case Study: Data Science at LeadCrunch

One of the biggest pain-points in real-world machine learning is to consistently and rapidly putting models into production. When LeadCrunch[ai] approached Verta.ai, their data science team was only able to deploy about two models annually. After…

ML-Infrastructure--Build-vs.-Buy-vs.-Open-Source

ML-Infrastructure: Build vs. Buy vs. Open-Source

Notes from TWIMLCon’s Unconference session

How to move fast in AI without breaking things

Model Versioning, Tracking, and Spreadsheets.

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