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Arise AI, a machine learning operations platform, announced today that it has raised $38 million in a Series B round led by TCV, along with Battery Ventures and Foundation Capital. Bringing Arize’s total capital to $62 million, the new cash will be used to boost R&D and double the company’s 50-person headcount next year, CEO Jason Lopateki said.
Machine learning operations, or MLOps, is concerned with deploying and maintaining machine learning models in production. Similar to DevOps, MLOps aims to increase automation by improving the quality of production models – but not at the expense of business and regulatory requirements. Given the widespread demand for machine learning and AI in the enterprise, it’s no surprise that MLOps is expected to be a huge market, with IDC putting the size at $700 million by 2025.
Ariz was founded in 2019 by Lopateky and Aparna Dhinakaran, after Lopateky sold his previous startup – TubeMogul – to Adobe for $550 million. Lopateki and Dhinakaran first met at TubeMogul, where Dhinakaran was a data scientist before joining Uber to work on machine learning infrastructure.
“After looking at group after group — year after year — of models being sent to production, we’ve come to the conclusion that something is fundamentally missing as we fail to understand what’s wrong with the models once they’re deployed and struggle to understand what they’re doing once they’re deployed,” Lopateki told TechCrunch. Email interview. “If the future is driven by AI, there must be software that allows people to understand AI, solve problems and fix it. AI without machine learning observability is unsustainable.”
Ariz is not the first to tackle these kinds of challenges in data science. Tacton, another MLOps provider, recently raised $100 million to build a machine learning model testing platform. Other players in the space include Galileo, Modular, Gantry and Grid.ai, the latter of which raised $40 million in June to feature components that add AI capabilities to applications.
Image Credits: He cried
But Lopateki says Ariz is unique in many ways. The first is focused on observation: Ariz’s embedding product is designed to observe deep learning models and understand their structure. It complements “bias tracing,” a tool that looks for biases in models (for example, facial recognition models recognize black people less than light-skinned people) — and tries to figure out the data that caused the bias.
Most recently, Arise launched embedding drift tracking, which tries to detect when the models become less accurate due to past training data. For example, the sliding tracking language model asks “Who is the current president of the United States?” If it answers “Donald Trump” in response to the question, it can alert the Arize client.
“Ariz stands out… [because] We’re laser-focused on doing one difficult thing well: machine learning observability.” Lopateki said. “Ultimately, we believe the machine learning infrastructure looks like a software infrastructure with multiple market-leading and best-of-breed solutions used by machine learning engineers to build great machine learning.”
Ariz’s second distinction, Lopatecki says, is his domain expertise. He and Dhinakaran learned from their backgrounds in academia and interns, he explains — building machine learning infrastructure and problem-solving models in production.
“Even for expert teams, it’s becoming impossible to keep up with every new model architecture and every new discovery,” Lopateki said. “As soon as teams finish building their latest model, they’re usually jumping on the next model that the business needs. This leaves little time to delve deeper into the billions of decisions these models are making every day and the impact these models have on both businesses and people… That’s why Ariz has spent over a year in production tracking deep learning models and designed workflows to find where they went wrong.”
Some may (correctly) argue that Ariz’s competitors have experts among their ranks and have visibility and tracking solutions across product portfolios. But judging by Ariz’s impressive customer list, the startup is making a heck of a compelling sales pitch. Uber, Spotify, eBay, Etsy, Instacart, P&G, TransUnion, Nextdoor, Stitch Fix and Chick-fil-A are among Ariz’s paying customers, and the company’s free tier — which launched earlier this year — has more than 1,000 users.
Mum is the term for annual recurring income. Lopateki insists that the capital from Sirib B will give the company “adequate runway”.
“In healthcare, there are groups using ARIZ to ensure consistency in production of cancer diagnostic models across a wide range of cancer types using imaging. Additionally, there are groups that use Arizen to model models used in routine care decisions and to ensure that insurance practices are consistent across racial groups. “As models become more complex, even the largest and most sophisticated machine learning teams are realizing that they would rather spend their time and energy on building machine learning observability tools… on investing in models and calculating the results for business leaders. [and provides] Market-leading software for managing the risks of AI investments.
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