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The third wave of operationalization is here: MLOps

Using machine learning models in products is hard. Most companies fail at extracting value from them because they can't operationalize models properly.

Verta: Model Management and Operations Platform for Production Machine Learning

Today, I’m thrilled to announce the formal launch of the Verta Model Management and Operations platform, and Verta’s $10M Series A funding led by Intel Capital. With the Verta Platform, we help data science teams to tame the chaos of brittle and fragmented…

Secure your machine learning platform

  Last week, the security community learned of a big cryptomining campaign that leveraged exposed Kubeflow clusters to mine Monero coins. This attack happened due to publicly exposed dashboards and has sparked a healthy conversation on the security of…

Happy birthday, Git!

Last April, Git celebrated its 15 year anniversary! It has by now become the defacto standard for versioning your code. It made many things much easier to do than its competitors and successfully created a vibrant and still growing ecosystem. However, the…

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, their data science team was only able to deploy about two models annually. After successfully…

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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