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Data science

Top 3 Reasons You Need a Model Registry

Today’s data scientists are often asked “How can you ship models to production faster without breaking things?”   Over the past few years, machine learning has seen exponential growth in enterprises. The emergence of tools that automate the model development…

How to Lead Data Science Teams to Collaborate Effectively

To motivate others to achieve the team’s mission and vision, you should possess the potential to motivate the inner prodigy inside you. Yes, you should be willing to learn and guide others to be a learner for life. That is the spirit of the most looked upon…

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…

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…


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

Notes from TWIMLCon’s Unconference session

How to move fast in AI without breaking things

Model Versioning, Tracking, and Spreadsheets.


Introducing Verta

Verta Company Blog
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