Molecula, which is developing a cloud-based feature store for AI and machine learning workloads, today announced it has raised $17.6 million. The company says the proceeds will be put toward accelerating the launch of its managed cloud service and bolstering its sales and marketing efforts.
Enterprises embracing an AI-first strategy must contend with federating, pre-aggregating, copying, and moving the data used to train their machine learning algorithms. In machine learning, features are data signals that AI models rely on to make accurate predictions. During training, features are stored in batches to train multiple variations, and the same features need to be available in real time during inference for predictions. Maintaining consistency between training and inference is often a challenge and can lead to inaccurate predictions or require coding.
Feature stores automate data prep for analytics and AI. In a recent report, startup Tecton said it expects 2021 to be a year of “massive feature store adoption” as “machine learning becomes a key differentiator for technology companies” and incumbents like Amazon launch new products to address the growing market segment. Molecula, the commercial version of the open source data format Pilosa, offers a cloud-agnostic data layer for big data analytics, AI, and machine learning. Molecula’s platform continuously extracts and updates features into a centralized feature store, ostensibly reducing the data footprint by 60% to 90% and providing a secure data format for sharing.
The company offers a framework that provides an interface for third-party tools, libraries, models, and code to extend the platform’s functionality. Its control plane allows users to operate in hybrid environments and take advantage of on-premises, cloud, and edge infrastructures. Meanwhile, “data taps” ingest and route data from source systems, optionally querying, selecting, and extracting them automatically.
“A machine learning revolution is taking place right now — businesses, no matter the industry, will need to implement ML and AI to remain competitive, but current infrastructures are far too complex,” Molecula CEO Higinio Maycotte told VentureBeat via email. “The feature store is emerging as the most transformative category in the data space because it automates the preparation of data for machine-scale analytics and AI. Molecula takes the feature store one step further by bridging the entire spectrum from data readiness to MLOps, making your most important data instantly computable.”
The series A funding round announced today brings Molecula’s total raised to $23.6 million. Drive Capital led the round, with participation from TTV Capital and existing investors, including Tensility.
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