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Machine learning startup Ensemble has increased $3.3 million in seed funding to address the growing importance of data quality in artificial intelligence. Salesforce Ventures led the round, with participation from M13, Motivate and Amplo.
Founders Alex Reneau And Zach Albertson are pioneering a new approach to data representation that promises to improve the performance of machine learning models without the need for large amounts of additional data or complex model architectures.
Unlocking hidden data relationships with ‘dark matter’ technology
“We have a new way to essentially access hidden relationships in your data or missing information that was originally in your dataset to improve your model,” said Alex Reneau, CEO of Ensemble, in an exclusive interview with VentureBeat. “We can enable customers to maximize the native data they work with, even if it is limited, sparse or highly complex, allowing them to train effective models with less extensive information.”
The company’s proprietary dark matter technology fits into the machine learning pipeline between feature engineering and model training. It creates enriched data representations that can reveal latent patterns and relationships, potentially making previously unsolvable problems tractable.
Addressing AI Adoption Challenges in Enterprises
This approach comes at a crucial time for AI adoption in enterprises. Despite rapid advances in AI capabilities, many organizations struggle to deploy models into production environments due to data quality issues.
Caroline Fiegel, an investor at Salesforce Ventures, explained the rationale behind their investment: “We may have seen over the last 12 to 24 months that companies are moving more slowly to AI and manufacturing than we expected,” she told VenutreBeat. “When you pull that back and really start to understand why, it’s because the data is disparate. It’s a bit low quality. It’s full of PII.”
Ensemble’s technology could have far-reaching consequences for all sectors. The company is already working with clients in biotechnology and advertising technology, with early results showing promise in areas such as predicting virus-host interactions in the gut microbiome.
From impossible to possible: expanding the horizons of machine learning
“We actually care much more about the cases where ML is able to do what was previously impossible,” Reneau pointed out. “So it’s not just about doing what a human can do and making it faster, but [it’s about] what a man could not do.”
The funding will be used to accelerate product development, expand the team and ramp up go-to-market efforts. As the AI landscape continues to rapidly evolve, Ensemble sees its role as providing a foundational technology that can adapt to changing needs.
“As these models continue to evolve and the data landscape will continue to evolve, I think we’re definitely more at the core of the research area,” Reneau said, referring to the company’s long-term vision.
For Salesforce Ventures, the investment is in line with their thesis about the crucial role of data in the adoption of AI. “Building trust in AI today is really based on results,” Fiegel said, “and having Alex and Zach share that core North Star with us keeps us excited.”
As companies grapple with the challenges of implementing AI at scale, Ensemble’s approach to data quality could prove to be a major enabler. The company’s progress will be closely watched by both the tech industry and the wider business community as a potential solution to one of AI’s most persistent obstacles.
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