SAN FRANCISCO, CA – [SEPTEMBER 9th, 2024] – Ensemble, a company dedicated to lowering barriers to state-of-the-art machine learning (ML), today announced it has raised $3.3M in seed funding, led by Salesforce Ventures with participation from M13, Motivate, and Amplo.
“This year, we launched a new embedding API that learns to approximate hidden relationships in data,” said Alex Reneau, CEO of Ensemble. “This foundational technology frees up data scientists to focus on experimentation and also makes ML viable for problems previously unable to be modeled, unlocking new capabilities for our customers.”
Don't Let Imperfect Data Get in the Way of Great Modeling
Ensemble believes in the value of machine learning to solve real-world problems, knowing the primary barrier to solving those problems is data. Limited, sparse, or high dimensional data can prevent ML models from producing meaningful results, and it is common for data scientists to spend the majority of their time addressing these data issues. Increasingly sophisticated models have been developed to compensate for these issues, but they require vast amounts of data, compute, and a team capable of a non-trivial implementation.
Ensemble is addressing the root cause — the data itself — with an embedding model that uses a new objective function to create the richest possible representation of data for each particular prediction task. This algorithm is able to account for complex, non-linear relationships through a lightweight data transformation. By distilling a high volume of complexity into a data representation, instead of using a model, data scientists and machine learning engineers can build high quality models in hard-to-model problem settings.
Ensemble’s embedding model seamlessly slots into the data science pipeline after feature engineering and before model training and inference, with a low code, low effort, and low impact addition to any ML effort that’s domain-agnostic and improves the performance of the end model. By speeding up the time it takes to get SOTA performance, problems can be solved quicker, accelerating deeper reliance on ML in a broader set of applications and enabling teams to focus on continuous innovation by doing more with less.
The Best Model is the Business Model
With this new funding, Ensemble is poised to accelerate its product development, expand its team, and reach customers across industries. The company is committed to pushing the boundaries of what ML can do and continuing to deliver frontier research in the field that translates into real-world impact. To understand how your organization could benefit from better ML performance, please reach out to set up a discovery call.
