While Yang predicted that AI would eventually become more production-ready, he admitted he didn’t think the AI application wave would arrive so soon. BentoML aims to be the Vercel for AI, he said. Yang compared his company to Vercel, which focuses on serving front-end developers and was last valued at over $1 billion.
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Users have also used the framework to run the art generator Stable Diffusion and open source LLMs on the cloud. With BentoML, developers can make Visual ChatGPT scalable and cost-efficient for production use in as short as two days, said Yang. Specifically, BentoML is targeting data scientists who train AI models, DevOp engineers who manage their lifecycle and developers who actually build applications on top of the models. It’s a so-called AI application framework, a set of tools that make it easier to build, ship and scale AI applications, like a construction tool kit one uses to build a house. While tech behemoths like Microsoft enjoy the financial prowess and human capital to train AI models and put them to use in the real world, smaller businesses, in Yang’s words, are collecting “valuable data that can benefit tremendously from AI” but “lack the resources to build the infrastructure for development.”īentoML, which provides a high-level API that abstracts away the details of the infrastructure needed for running AI models on the cloud, belongs to a camp of tools like SageMaker that wants to smooth the path for developing AI services.
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Many programmers entering the fray are coming from a full-stack or application development background, meaning they often lack the skills to build the required AI infrastructure, resulting in a prolonged development process.Ī demo AI app like Microsoft’s Visual ChatGPT, an upgrade to the chatbot that allows it to produce responses from both text and image prompts, for example, can take at least three to six months to make it production-ready, Yang said.
Yang, in an interview with TechCrunch, explained that today’s AI services are often built on multiple machine learning models, making their management and operation complicated. Along with his co-founders, he’s built the AI development framework BentoML, which just announced a seed financing round.
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That’s where Chaoyu Yang, an early software engineer at the data mega-unicorn Databricks, comes in. But building AI services can be tricky, especially due to the shortage of skilled developers to meet the rising demand these days. The success of large language models like GPT has sparked a frenzy of developers eager to make AI-powered applications.
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