In a move that reflects the growing interest in MLOps (Machine Learning Operations), Striveworks, an Austin-based startup building tools to handle the complexities of machine learning models, has announced $33 million in funding. This influx of capital underscores both the surge of interest in artificial intelligence and Striveworks’ own traction within the industry.
A Growing Market
MLOps, often described as DevOps for machine learning, has gained significant attention over the past year. Organizations are eager to integrate more machine learning into their data science stacks, but this requires building and training models, cleaning up data, and ensuring they function as expected. Striveworks’ flagship platform, Chariot, aims to tackle these challenges by providing a low-code solution for teams to collaborate on model preparation, development, and deployment.
Striveworks: A "Capital-Efficient" Startup
Founded five years ago by Jim Rebesco, Craig Desjardins, Eric Korman, and Tony Manganiello, Striveworks has managed to grow at an impressive rate, with ARR increasing 300% annually over the last two years. This capital-efficient approach has allowed the company to operate profitably without external funding until now.
Partnerships and Customers
Striveworks has established partnerships with AWS and Azure to work on data in these clouds, catering to highly regulated industries such as government, finance, and national security applications. Notably, the company currently lacks a partnership with Google. Rebesco shared that customers span various verticals, including those mentioned above, without disclosing specific names.
The Challenges of Machine Learning
Rebesco, an neuroscience PhD with a background in financial services, highlighted the difficulties encountered at previous companies: "The day-one problem is building appropriate machine learning models to fit one’s objectives. But that’s in some ways the easy part." He emphasized the importance of addressing model performance post-deployment, saying, "We focus on what happens next."
Tackling Model Failures
Rebesco shared a crucial lesson learned from his pre-PhD work: AI models can fail due to various reasons. He stressed that understanding and addressing these failures is essential for reliable machine learning deployment.
A Rare Occurrence: Funding at Scale Without External Capital
Ben Cukier, the investor leading this round, described Striveworks’ achievement as a rare occurrence in his 27 years of investing. "These are real customers, with seven-figure contracts, and net retention numbers that would be the envy of many other companies." He believes that the current market is returning to normal after several intense years.
Conclusion
Striveworks’ $33 million funding round underscores the growing importance of MLOps in the AI landscape. As organizations increasingly rely on machine learning solutions, startups like Striveworks are poised to play a significant role in addressing the complexities associated with model deployment and management.
AI, Enterprise, MLops
Ingrid Lunden is Europe Editor at TechCrunch. She joined February 2012 based out of London. Before TechCrunch, Ingrid worked at paidContent.org as a staff writer, and has freelanced for other publications such as the Financial Times.
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