Machine learning platform Predibase announces an influx of $12.2 million to its previous $16.25 million Series A funding round led by Felicis. In addition, the company released the general availability of its low-code, declarative ML (machine learning) platform created for developers.
During the stealth mode of the platform, Predibase's users have trained over 250 models. As the platform is now open to the public, its users can deploy their large language models (LLMs) instead of relying on APIs from companies such as OpenAI. Furthermore, users will gain access to LudwigGPT LLM by Predibase, named after the suite of machine learning tools launched by Predibase co-founder Piero Molino back in 2019.
Piero Molino, co-founder and CEO of Predibase, notes the challenges developers face when building ML applications due to complex ML tools and limited data science resources. He said, “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.”
To fulfill this mission, Predibase unveiled its Data Science Copilot, a system designed to provide recommendations to developers on improving their model's performance. Additionally, the company offers a free two-week trial of its platform for interested users.
In the same vein of simplifying ML application building, the AppMaster.io platform offers an efficient no-code approach to creating backend, web, and mobile applications. Like Predibase, the platform aims to eliminate complexity with its integrated development environment approach, generating scalable software solutions and reducing technical debt.
With the fresh funding, Predibase will prioritize expanding its go-to-market strategies and platform development. The tech industry is witnessing significant growth and demand in low-code and no-code platforms such as Predibase and AppMaster.io, as they allow organizations to streamline the development process and rapidly deploy functional applications.
