An artificial intelligence business located in San Francisco called Galileo has released a new product called Galileo LLM Studio. By enhancing model accuracy and identifying “model hallucinations,” or false predictions, the platform seeks to speed up the deployment of natural language processing models into production at various businesses.
Yash Sheth, co-founder of Galileo, said in an exclusive interview with VentureBeat: “We firmly think that generative AI is set to revolutionise the world. Predictive machine learning has made it feasible for businesses, governments, and people to engage with AI in ways that were before impossible.
Businesses are keen to employ the models for chatbots, intelligent search, and automatic text production, so the platform’s release is timely. Building and releasing such sophisticated models, however, continues to be difficult. Sheth claims that data scientists devote a significant portion of their time on “data cleaning,” or rectifying problems inside datasets in order to boost the precision of their models.
“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 almost a decade of work on machine learning at Google. This was the general situation in the artificial intelligence sector when we first looked outside.
Quickening the rate of adoption
Galileo’s technology is designed to streamline and speed up data cleansing processes. Using the Galileo Prompt Studio, data scientists may quickly rectify mistakes by identifying “model hallucinations,” or inaccurate predictions. The software also provides an estimate of the cost of calls to external AI services like OpenAI, allowing data scientists to better manage their budgets.
Sheth maintains that knowing how data will influence and modify generative models is essential to realising their full potential in light of the market commoditization of such models. It’s a lengthy process to modify and implement such models. He said that “anything we can do to speed that up will only speed up adoption of AI around the world.”
The company has ambitions to grow into other areas of artificial intelligence, such as computer vision, in addition to natural language processing. In the end, we embed inside neural networks, and the neural networks’ representation of the input is simply a vector of floats, thus our methods can handle data in any format, as Sheth put it.
Galileo is prepared to capitalise on the growing demand for useful AI tools thanks to the $18 million in financing it has received from investors including Battery Ventures. Competitors like as Google, Microsoft, and Amazon Web Services (AWS) all provide infrastructure for developing and managing AI models, so business isn’t easy. Galileo thinks it can stand out from the crowd by concentrating on finding and correcting mistakes in its models.
For AI to be widely adopted, organisations need to “be data centric” and “have a key model diagnostic view across the ML lifecycle,” as Sheth put it.