Verta is a Palo Alto-based technology company that provides software infrastructure designed to help enterprise data science and machine learning (ML) teams efficiently develop, deploy, and manage ML models. The Verta platform builds upon pioneering research from MIT CSAIL, specifically ModelDB, which was the first open-source model management system and is now used by multiple Fortune 500 companies.
Verta extends ModelDB's capabilities to support the entire machine learning lifecycle, including model training, deployment, and governance. This enables data science teams to streamline the process of moving models from development to production, while ensuring robust tracking and compliance throughout.
How was Verta started?
Verta was founded by a leadership team with expertise in enterprise technology, product development, and data science. The founding team includes Charles Sansbury (Chief Executive Officer), Abhas (Chief Strategy Officer), Leo Brunnick (Chief Product Officer), Irma Laxamana (Chief Legal Officer), Amy Nelson (Chief Human Resources Officer), Frank O’Dowd (Chief Revenue Officer), Dilip Upmanyu (Chief Financial Officer), and Mary Wells (Chief Marketing Officer).
Who uses Verta?
Verta serves enterprise and Fortune 500 organizations looking to manage and scale their machine learning initiatives. Their platform is used by a wide range of customers, including:
- IQVIA
- US Centers for Disease Control and Prevention
- Regions Bank
- Intel
- LG U+
- Tsingtao
- Continental
- LY Corp
- Dell Technologies
- SAIC Volkswagen
- GEODIS
- Price Waterhouse Cooper
- Mastercard
- British Telecom
- ExxonMobil
- Mercy Corps
- OCBC Bank
- Continental Tires
- BT Group
- Center of Disease Control
- Amdocs
- FairConnect
This diverse customer base highlights Verta's focus on providing scalable ML infrastructure for industries such as healthcare, finance, telecommunications, manufacturing, and more.
What makes Verta different?
Verta's differentiation lies in its foundation on ModelDB research from MIT and its comprehensive approach to the ML lifecycle—from model versioning and experiment tracking to deployment and governance. This allows organizations to maintain transparency, reproducibility, and compliance across their ML workflows.
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