Patterns, backed by Y Combinator, builds a platform to summarize busy data science work

Ken Van Haren and Chris Stanley were data scientists at Google and Square, respectively, who found themselves frustrated with the time they spent arguing over infrastructure rather than doing real data science. By surveying their colleagues, they found that this was a common problem. According to a surveydata scientists spend more than half of their time cleaning and organizing data and the majority of the rest collecting datasets.

Aiming to streamline grunt work, Van Haren and Stanley launched Grounds, a platform that abstracts AI model engineering. Backed by Y Combinator and angel investor Lenny Rachitsky, Patterns recently closed a $2.5 million pre-seed round.

“Patterns is the platform for any leader looking to prepare for the new world of AI, stay ahead of the transformation it will bring to their business, and start integrating AI capabilities. in its product and operations,” Van Haren told TechCrunch in an email interview. . “We help companies manage the incredible speed of advancement in AI, which means adapting quickly to new models and paradigms.”

Patterns’ platform allows users to build AI integrations, automations, and workflows from a set of modular components. According to Van Haren, it essentially wraps workflow logic and infrastructure in a software layer.

First, customers connect an application to Patterns using a library of predefined connectors. Then they create a use case with code in the Patterns website EDI — ship the final product and possibly monitor its performance with the platform’s analysis and debugging tools.


The Patterns platform. Picture credits: Grounds

So what can you build with Patterns? Van Haren gave examples from his own experimentation. By using great language models like ChatGPT, it built a free-form Q&A robot on top of a CrunchBase database of investors, companies, and fundraisers. For another demo, Van Haren tweaked OpenAI GPT-3 language model on a dataset of over 6.5 million Hacker News comments – in his own words – “to represent the collective wisdom of the HN community in a single bot”.

“People are excited about AI and looking to go beyond just playing in a playground,” Van Haren said. “Models give them a fast and powerful way to develop and deploy AI in real-world problems.”

Patterns contains elements of an MLOps platform, i.e. a platform for building, testing, and deploying machine learning models in production. MLOps is a booming field, with many vendors vying for both market share and venture capital dollars.

By a estimatethe MLOps market could reach $4 billion by 2025.

There is Galileowhich provides a platform for the development of AI models, and Qwak, whose fully managed platform combines machine learning engineering and data management tools. Other rivals in the space include companies oriented Diving plane, Tecton, He is crying, Iterative, Comet And Weights and biases.

Despite the competition, Van Haren says Patterns has had no trouble attracting users, growing its base to around 1,500 today. (He didn’t reveal the percentage of paying customers, but he said Patterns planned to enter into a government contract later this year.)

Patterns’ immediate plans are to increase its workforce, which currently stands at four full-time employees, including Van Haren and Stanley.

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