Manasi Vartak

Manasi Vartak  •  May 18, 2023

5 Signs You Need a Model Catalog

Enterprises are adopting model management techniques and model catalogs as part of their journey toward Operational Excellence in AI and machine learning.

Manasi Vartak  •  Nov 8, 2022

Key Enterprise Technology Trends Impacting ML Infrastructure Today

ML infrastructure has five themes in enterprise technology and machine learning - resiliency, risk, real time and other key trends. Read more for details.

Manasi Vartak  •  Jul 11, 2022

Using CI/CD to Bring Models to Production: More Notes from Data+AI Summit 2022

See examples of applying CI/CD pipeline to ML models and how to take a software delivery pipeline and convert it to a machine learning delivery pipeline.

Manasi Vartak  •  Jul 8, 2022

Operationalization, Model Catalogs, and an EHR for Models at Data+AI Summit

Learn about model operationalization and model catalogues, the greater focus on them, full lifecycle model management, and the need for an EHR for models.

Manasi Vartak  •  Mar 10, 2022

What is a Model Catalog?

So what is a model catalog? This post dives into the question by sharing its benefits and how it differentiates itself from a model registry.

Manasi Vartak  •  Dec 17, 2020

How to Lead Data Science Teams to Collaborate Effectively

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

Manasi Vartak  •  Nov 19, 2020

Serverless for ML Inference on Kubernetes: Panacea or Folly?

Serverless ML: We present benchmarking results on ML serving on serverless architectures and traditional computing architectures. Is this Panacea or Folly?

Manasi Vartak  •  Aug 25, 2020

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.

Manasi Vartak  •  May 13, 2020

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

Robust MLOps with Open-Source: ModelDB, Docker, Jenkins and Prometheus. What to borrow from DevOps & a hands-on exercise in building a real-world MLOps pipeline.

Manasi Vartak  •  Apr 10, 2020

Making your Scikit-learn models reproducible with ModelDB 2.0

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

Manasi Vartak  •  Nov 26, 2019

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 and what was the impact.

Manasi Vartak  •  Oct 8, 2019

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

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

Manasi Vartak  •  Jun 28, 2019

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.

Manasi Vartak  •  Feb 21, 2019

Introducing Verta

We are building software in the service of models. Rapidly develop & deploy production-ready models for efficient integration of ML into diverse products.