MLOps Trailblazer Featureform Raises $5.5MM by Refining and Accelerating the Way Teams Work on AI and ML

With the vast amount of LLMs being used by enterprise companies, the Feature Store is here to save data scientists by simplifying big data workflows

SAN FRANCISCO, Dec. 13, 2023 /PRNewswire/ — Featureform, the MLOps feature store for building AI and ML systems, has announced $5.5MM in Seed funding led by GreatPoint Ventures and Zetta Venture Partners with participation from Tuesday Capital and Alumni Ventures. This round of capital allows Featureform to expand its product growth and increase support for existing and new enterprise customers, in addition to its open-source community. The completion of the Seed round brings Featureform’s total funding to date to $8.1MM.

At enterprise companies, LLM usage has surged alongside traditional ML use cases. At the heart of both these systems is private data. The most critical thing that ML teams do is take their raw data and transform it into valuable signals to feed into LLMs via prompts or ML models as inputs. Featureform believes there needs to be a unified framework to define, manage, and deploy these signals (or features). This creates a unified resource library that can be used by all ML/AI teams across an organization with built-in search & discovery, monitoring, orchestration, and governance. Featureform has seen to this be true with their existing customers in the ML space and has begun spearheading this approach in the LLM space.

“MLOps is moving out of the hype phase and entering the actual productivity phase,” says Featureform Founder and CEO Simba Khadder. “On the backend of this, we’re seeing a huge wave of new use-cases that have been unlocked with LLMs. Data is at the core of these two systems, and in practice, the problems look almost identical. Featureform’s frameworks will fundamentally change the way ML and AI teams work with data.”

The rise of Retrieval Augmented Generation architecture, or RAG, has provided a way for data scientists to inject relevant signals and content from their data sets into their prompts as content to increase an LLMs accuracy and decrease likelihood of hallucination. These signals are analogous to traditional machine learning features that you’d find in a feature store. The big difference is that, after being processed, they are stored in a vector database. By adding vector database support, Featureform becomes the hub where data scientists can define, manage, and deploy their features for both ML and LLM systems.

“Featureform’s feature store platform offers a distinct advantage in the market with its unique virtual architecture,” says Gautam Krishnamurthi, Partner at GreatPoint Ventures. “This virtual approach not only sets them apart from the competition, but also significantly lowers the cost of feature store implementation in the large and growing MLOps market. Coupled with their expert team, Featureform provides a best-in-class solution in the market for building out machine learning feature management. We are excited to support the Featureform team in their Seed round and beyond!”

Featureform provides data scientists with a framework to turn their data into useful features for ML models and LLMs. By using Featureform, these teams:

• Accelerate time to build and deploy new features from months to hours.
• Deduplicate and leverage existing work by allowing data scientists to collaborate on, share, and discover features.
• Improve models in production by guaranteeing consistency between serving and training data, and catch feature drift before it becomes a problem.
• Effortlessly enforce access control and governance policies in the feature workflow.

To learn more visit https://featureform.com.

About Featureform
Featureform is the creator of the virtual feature store. Our mission is to streamline how data and model features are built and maintained in machine learning orgs. Our python framework and feature store does away with copy and pasting between scattered notebooks with names like “Untitled18.ipynb,” unifies feature pipelines between experimentation and production, deduplicates repeated features across teams, and eliminates ambiguously named tables like “feature_table_v5.” While we pride ourselves on our open-core model, we also offer a robust enterprise solution with governance, streaming, and more. We are proudly based out of San Francisco. Discover more about how we’re reshaping the MLOps landscape at https://featureform.com.

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