Galileo launches LLM Studio to revolutionize AI adoption in enterprises

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    Galileo, a San Francisco-based artificial intelligence startup, announced today the launch of Galileo LLM Studio, a platform to diagnose and fix issues with large language models. The platform aims to help companies deploy natural language processing models into production faster by detecting “model hallucinations,” or incorrect predictions, and improving model accuracy.

    In an exclusive interview with VentureBeat, Yash Sheth, co-founder of Galileo, explained the vision behind LLM Studio: “We truly believe that generative AI is poised to change the world. Enterprises, governments, and individuals can now finally interact with AI in ways that were not possible with predictive machine learning.”

    The platform comes as demand for natural language processing has skyrocketed, with businesses eager to use models for applications like chatbots, intelligent search, and automated text generation. However, building and deploying these complex models remains challenging. According to Sheth, data scientists spend much of their time on “data cleaning,” fixing issues in datasets to improve model accuracy.

    “Despite having the best talent, the best team, the best infrastructure, it took us months to launch one model into production,” said Sheth, reflecting on nearly a decade of working on machine learning at Google. “When we started looking outside, this was the status quo across the AI industry.”

    Accelerating the pace of adoption

    Galileo’s platform aims to automate much of the work typically spent cleaning datasets. The Galileo Prompt Studio detects “model hallucinations,” or incorrect predictions, enabling data scientists to address errors faster. The platform also allows data scientists to compare multiple prompts to find the optimal input, and estimates the cost of calls to external AI services like OpenAI to help manage budgets.

    With generative models becoming increasingly commoditized, Sheth believes that the key to unlocking their potential lies in understanding how data will impact and adapt these models. “It takes a long time to really adapt these models and make them work. Anything we can do to accelerate that will only accelerate adoption of AI around the world,” he said.

    The startup also hopes to expand beyond natural language processing to other AI domains like computer vision. “Our algorithms span across data formats, because in the end, we embed within neural networks and the neural networks’ representation of the data is just a vector of floats,” Sheth said.

    With $18 million in funding from investors including Battery Ventures, Galileo is poised to capitalize on booming demand for practical AI tools. However, the company faces stiff competition from tech giants like Google, Microsoft and AWS, who also offer platforms to build and manage AI models. Galileo hopes its focus on diagnosing and fixing model errors will differentiate them.

    “Being data centric, and having a key model diagnostic view across the ML lifecycle is absolutely critical for the adoption of AI,” Sheth said.


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