Abacus.AI is announcing $22M in Series B funding led by Coatue. Decibel Ventures and Index Partners also participated in this round. As part of this release, the company is announcing Abacus.AI Deconstructed, which is a suite of 3 stand-alone tools that can be used by organizations to bring AI models to production.
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Series B funding
With this round the company has raised $40.3M in total funding in less than two years. Yanda Erlich, General Partner at Coatue, is joining Abacus.AI’s board of directors. “We are proud to be leading the Series B investment in Abacus.AI,” said Erlich, “because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups. Abacus.AI’s end-to-end autonomous AI service, powered by their Neural Architecture Search invention, helps organizations with no ML expertise easily deploy deep learning systems in production.”
Mike Volpi from Index Ventures and Jon Sakoda from Decibel also participated in this round. “We are excited to continue to invest in Abacus.AI and support their mission to democratize AI and make state-of-the-art deep learning systems available in a plug and play fashion to organizations of all sizes,” said Volpi.
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Abacus.AI Deconstructed
Abacus.AI offers a state-of-art autonomous deep learning service that automates all aspects of machine learning, from model creation to deployment and maintenance. As part of this release, the company is announcing Abacus.AI Deconstructed. Deconstructed separates parts of Abacus.AI’s underlying platform and offers them as stand-alone modules. Today, when less than 1% of models trained are actually put into production, these 3 services will help organizations quickly deploy and maintain models in production.
Model Hosting and Monitoring – Organizations can easily host their models in production with this module. This module helps teams deploy, maintain and govern them all in one place that will assist with the messy issues of production operations. Monitoring models for drift is essential for maintaining ML models in predictions and knowing when to trigger re-training runs. This module alerts production teams when models experience prediction and data drifts.
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