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Model management

How to Deploy ML models with Google Cloud Run

This blog post is part of our series on Serverless Inference for Machine Learning models accompanying our KubeCon 2020 talk: Serverless for ML Inference on Kubernetes: Panacea or Folly? - Manasi Vartak, Verta Inc. We'll be hosting a live replay on December…

How to Deploy ML models with AWS Lambda

This blog post is part of our series on Serverless Inference for Machine Learning models accompanying our KubeCon 2020 talk: Serverless for ML Inference on Kubernetes: Panacea or Folly?  We'll be hosting a live replay on December 2nd at 2 PM ET for anyone…

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.

Model Management & 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…

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…

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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