Highlights:

  • The introduction of its platform coincided with the announcement of a USD 12.2 million Series A funding round expansion headed by Felicis Ventures Management Co., LLC.
  • Predibase’s mission, according to Molino, is to make it easier for novices and experts equally to create and deploy machine learning applications, including large language models for generative AI applications.

Predibase Inc., the Machine Learning startup, announced the commercial availability of its low-code declarative ML platform for AI developers, adding new features for large language models.

The introduction of its platform coincided with the announcement of a USD 12.2 million Series A funding round expansion headed by Felicis Ventures Management Co., LLC.

Predibase is designed to aid developers and data scientists in constructing, iteration, and deploying sophisticated AI models and applications. It intends to assist smaller businesses in competing with large corporations like Meta Platforms Inc., Apple Inc., and Uber Technologies Inc. It claims to accomplish this by eliminating the need for complex ML tools and assembling low-level machine learning frameworks.

Using Predibase’s ML platform, teams need only to define what they wish to predict using a selection of prebuilt, large AI models, and the platform will take care of the rest. Experienced practitioners can utilize the platform to fine-tune a model parameter, while novice users can leverage recommended model architectures to get started.

Thus, according to Predibase, the time required to deploy machine learning models can be reduced from months to days. The company claims that more than 250 models have been trained on its platforms since exiting stealth mode last year.

The company could not have launched its platform at a better moment, with the rise of generative AI models such as OpenAI LP’s ChatGPT captivating the imagination of virtually every business today. In recent months, companies have rushed to implement generative AI capabilities to obtain a competitive edge.

Piero Molino, Co-founder and CEO of Predibase, told one of the leading media houses that businesses are frantic to incorporate ML capabilities into their internal and client-facing applications. The issue, according to him, is that the majority of ML development tools are too complicated for engineering teams to use, and their data science resources are already strained too thin. Consequently, Predibase employs what it refers to as a “declarative” approach to machine learning development.

Piero Molino said, “Declarative means you can specify what you want the ML models to predict, and from which data, without having to specify the how. In practice, this means writing a few lines of a configuration YAML file that matches the schema of the data and declares what you want the model to predict, as opposed to writing thousands of lines of low-level machine learning code to achieve the same. Think about what Terraform does for infrastructure. It’s the same approach applied to machine learning.”

Predibase’s mission, according to Molino, is to make it easier for novices and experts equally to create and deploy machine learning applications, including large language models for generative AI applications. To achieve this, it enables users to construct on top of open-source LLMs such as Ludwig and Horovod, which are continuously developed and enhanced by the community, and then fine-tune these models to their specific requirements.

Molino said, “The problem with the open-source models is that companies would need to figure out how to serve them, how to adapt them to their tasks, and how to deploy them in a cost-effective way. Predibase caters to all three of those needs by making fine-tuning and deployment just a matter of writing a simple declarative YAML configuration that any developer can write.”

The most recent version of Predibase also includes a new, AI-powered data science copilot tool that offers developers recommendations on enhancing the efficacy of the models they are creating, as well as real-time explanations and examples.

Unique Approach

Andy Thurai, vice president and principal analyst at Constellation Research Inc., told a prominent media outlet that Predibase’s declarative approach to ML is unprecedented because it has never been attempted before.

Andy Thurai said, “Rather than building models from scratch, users can compose the underlying workflows, architecture and tools rapidly to set up an experimentation environment. Predibase combines that with low-code options to enable the deployment of models in production quickly, using various templates. It has the potential to bring machine learning to the masses, as opposed to relying on costly data scientists. It may be a good option for enterprises that are short of good data science resources or need to produce models and deploy them faster than their existing data science teams can do.”

Predibase has released a free, two-week trial version of its platform to demonstrate its value, allowing any business to observe how its declarative approach can accelerate model development. The free trial is offered as a fully-hosted software-as-a-service through the Predibase Cloud or as a virtual private cloud in the customer’s environment. Customers will have access to LudwigGPT, the custom LLM that enables Predibase’s data science copilot, as part of the trial.

While in beta, the platform has been extensively tested by several businesses. Wells Fargo & Co.’s data science chief, Dr. Volkmar Scharf-Katz, is one of its greatest proponents. The platform, according to him, combines the simplicity of an AutoML platform with the robust flexibility and sophisticated features that data scientists desire.

Volkmar Scharf-Katz said, “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.”

The new investment from Felicis brings Predibase’s Series A funding round to USD 25.2 million, bringing the company’s total funding to date to USD 28.5 million, according to the company. Predibase stated that it would expand its go-to-market operations and develop new platform capabilities with the additional funds.