SAN FRANCISCO--(BUSINESS WIRE)--Predibase, the first commercially available low-code declarative ML platform for developers, today announced the general availability of its platform, adding new features for large language models and introducing free trial editions. In addition, the company also announced a $12.2 million expansion of its Series A funding round led by Felicis.
Predibase makes the extremely powerful but proprietary declarative ML approaches adopted by companies like Uber, Apple and Meta available to a much wider audience. In production with Fortune 500 organizations and high-growth startups like Paradigm and Koble.ai, the proven Predibase platform enables developers and data scientists alike to quickly and easily build, iterate and deploy sophisticated AI applications without the need to learn how to use complex ML tools or assemble low-level ML frameworks. Teams simply define what they want to predict using Predibase’s cutting-edge large AI models and the platform does the rest. Novice users can leverage recommended model architectures, while expert users can finely tune any model parameter. As a result, Predibase cuts the time to deploy ML-powered applications from months to days. Since coming out of stealth, over 250 models have been trained on the platform.
“Every enterprise wants to gain a competitive edge by embedding ML into their internal and customer-facing applications. Unfortunately, today’s ML tools are too complex for engineering teams, and data science resources are stretched too thin, leaving the developers working on these projects holding the bag,” said Piero Molino, co-founder and CEO of Predibase. “Our mission is to make it dead simple for novices and experts alike to build ML applications and get them into production with just a few lines of code. And now we’re extending those capabilities to support building and deploying custom LLMs.”
The GA version of Predibase adds new capabilities, including:
- Privately Hosted, Customized Large Language Models – instead of renting often costly large-language models (LLMs) from API vendors and giving up access to their most sensitive data, Predibase allows organizations to deploy their own LLMs securely within their own enterprise infrastructure, finely tuned for their specific ML task. Additionally, Predibase provides optimizations to accelerate LLM tuning while reducing costs by 100x.
- Data Science Copilot – gives developers expert recommendations on how to improve the performance of their models, as well as explanations and examples in real-time as they iterate.
According to Dr. Volkmar Scharf-Katz, Data Science Leader at a leading U.S. financial institution, “Predibase’s declarative ML platform shines with the simplicity of an AutoML platform while providing the robust flexibility and advanced features that data scientists desire, like support for Python scripts and model fine-tuning. It's stunning to see how fast accurate results can be delivered - reducing time to value from months to days. Moreover, Predibase allows different personas to work with the platform serving many use case scenarios in regulated domains like finance and healthcare.”
Along with the GA launch, Predibase is introducing a free two-week trial version of the platform to give engineering and data science teams an opportunity to see how the declarative approach can accelerate their ML development. The free trial is offered as a fully hosted SaaS solution in the Predibase Cloud or via VPC in the customer’s environment. To demonstrate how easy it is to build a custom large language model on Predibase, the free trial includes access to LudwigGPT, a custom LLM built using Predibase to power the platform’s Data Science Copilot.
“With over $200B in trades, Paradigm is the largest global liquidity network for cryptocurrencies. One of our top priorities is helping traders make smarter decisions with AI,” said Anand Gomes, co-founder and CEO of Paradigm. “Predibase has enabled our team of engineers to build new product capabilities that were previously not possible. We’ve built powerful relevance scoring and in-platform intelligence that helps our customers identify trading opportunities and capture edge. Best of all, the time it takes to build production models has been reduced from months to minutes and at a fraction of the cost.”
The expansion of Predibase’s Series A round, led by Felicis, brings the round up to $25.2 million and total funding for the company to date to $28.5 million. The additional funds will be used to expand Predibase’s go-to-market function and add new capabilities to the platform.
”For machine learning to become pervasive, it will have to be much simpler for just about every organization to deploy than it is today,” said Niki Pezeshki, General Partner at Felicis. “After seeing how much traction Predibase has gained in the year since launch and how their platform has been transformative for their customers’ ML projects, we believe they’ve cracked the code.”
About Predibase
Predibase’s mission is to make state-of-the-art ML easy for every organization. Like Infrastructure as Code simplified IT, Predibase’s ML platform allows users to focus on the “what” of their ML models rather than the “how,” breaking free of the usual limits in low-code systems and bringing down the time-to-value of ML projects from years to days. For more information, go to http://www.predibase.com or follow @predibase.
About Felicis
Founded in 2006, Felicis is a venture capital firm investing in companies reinventing core markets, as well as those creating frontier technologies. Felicis focuses on early-stage investments and currently manages over $3B in capital across 9 funds. The firm is an early backer of more than 47 companies valued at $1B+. More than 100 of its portfolio companies have been acquired or gone public, including Adyen (IPO), Credit Karma (acq by Intuit), Cruise (acq by General Motors), Fitbit (IPO), Guardant Health (IPO), Meraki (acq by Cisco), Ring (acq by Amazon), and Shopify (IPO). The firm is based in Menlo Park and San Francisco in California. Learn more at felicis.com.
