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The model registry is a system that allows data scientists to publish their production-ready models and share them for collaboration with other teams and stakeholders. MLOps cannot be done right until you have a state-of-the-art Model Registry.

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Example Post

The model registry is a system that allows data scientists to publish their production-ready models and share them for collaboration with other teams and stakeholders. MLOps cannot be done right until you have a state-of-the-art Model Registry.

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Top 3 Reasons You Need a Model Registry

The model registry is a system that allows data scientists to publish their production-ready models and share them for collaboration with other teams and stakeholders. MLOps cannot be done right until you have a state-of-the-art Model Registry.

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How to Lead Data Science Teams to Collaborate Effectively

Managing data science teams successfully is challenging and rewarding at the same time. The best data scientists would be intolerant to bad managers.

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Serverless for ML Inference on Kubernetes: Panacea or Folly?

We present benchmarking results on ML serving on serverless architectures and traditional computing architectures.

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How to deploy ML models on the Verta Platform

How to deploy models on Verta, including pros and cons of using this system for inference. We'll also demonstrate how to run DistilBERT on Verta.

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How to Deploy ML models with Google Cloud Run

In this series of posts, we cover how to deploy ML models on each of the above platforms and summarize our results in our benchmarking blog post.

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How to Deploy ML models with AWS Lambda

This post talks about how to get started with deploying models on AWS Lambda, along with the pros and cons of using this system for inference.

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The third wave of operationalization is here: MLOps

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

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Model Management & Operations Platform for Production Machine Learning

Series-A funding and launch of Verta Model Management 7 Operations Platform, helping data science teams to tame the chaos of workflows in machine learning.

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Secure your machine learning platform

Inspired by the recent Kubeflow exploit for cryptomining. How attackers can take over your ML platform, and how to secure your machine learning platform,

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Happy birthday, Git!

Why Git won the battle, its shortcomings, how the ecosystem has improved to counter them, and the key takeaways we need for machine learning.

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

Understand the need for MLOps, what we can borrow from DevOps, and get access to a hands-on exercise in building a real-world MLOps pipeline.

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Making your Scikit-learn models reproducible with ModelDB 2.0

Get a quick peek into the functionality provided by ModelDB 2.0 and how it can be used to make models (or analyses) reproducible.

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ModelDB 2.0 is here!

We are excited to announce that ModelDB 2.0 is now available!

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Case Study: Data Science at LeadCrunch

This case study explores how LeadCrunch successfully implemented Verta.AI and sped up model production 5x. Learn how they did it.

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

Learn whether to build ML infrastructure in-house, whether to buy it, or whether to just leverage open-source.

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How to move fast in AI without breaking things

Model versioning is an often-overlooked but critical issue in AI & ML, one without which we cannot begin to trust models to run our products and businesses

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Introducing Verta

Rapidly develop and deploy production-ready models, thereby enabling efficient integration of ML into diverse products.

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