AI News sat down with Piero Molino, CEO and co-founder of Predibase, during this year’s AI & Big Data Expo to discuss the importance of low-code in machine learning and trends in Large Language Models (LLMs).
At its core, Predibase is a declarative machine learning platform that aims to streamline the process of developing and deploying machine learning models. The company’s mission is to simplify and democratize machine learning, making it accessible to both experienced organizations and developers who are new to the industry.
The platform provides organizations with in-house experts, enabling them to enhance their capabilities and reduce development time from months to days. It is also aimed at developers who want to integrate machine learning into their products but lack the skills.
Using Predibase, developers can avoid writing long lines of low-level machine learning code and instead work with a simple configuration file known as a YAML file that contains just 10 lines specifying the schema of the data.
Predibase reaches general availability
During the show, Predibase announced the general availability of its platform.
One of the key features of the platform is its ability to abstract the complexity of infrastructure provisioning. Users can seamlessly run training, deployment, and inference jobs on a single CPU machine or scale up to 1000 GPU machines with just a few clicks. The platform also facilitates easy integration with various data sources, including data warehouses, databases, and object stores, regardless of data structure.
The platform is designed for teams to collaborate on model development, with each model represented as a configuration that can have multiple versions. You can analyze the differences and performance of the models, explains Molino.
Once a model meets the required performance criteria, it can be deployed for real-time predictions as a REST endpoint or for batch predictions using SQL-like queries that include prediction capabilities.
Importance of low-code in machine learning
The conversation then turned to the importance of low-code development in machine learning adoption. Molino emphasized that streamlining the process is essential for broader industry adoption and greater return on investment.
By reducing development time from months to days, Predibase lowers the barrier to entry for organizations to experiment with new use cases and potentially unlock significant value.
If each project takes months or even years to develop, organizations will have no incentive to explore valuable use cases. Lowering the bar is critical to experimentation, discovery and increasing return on investment, says Molino.
With a low-code approach, development time is reduced to a couple of days, making it easier to try out different ideas and determine their value.
Trends in LLMs
The discussion also touched on the growing interest in large language models. Molino recognized the enormous power of these models and their ability to transform the way people think about artificial intelligence and machine learning.
These models are powerful and revolutionize the way people think about AI and machine learning. Previously, you needed to collect and label data before training a machine learning model. But now, with APIs, people can query the model directly and get predictions, opening up new possibilities, Molino explains.
However, Molino did highlight some limitations, such as the cost and scalability of per-query pricing models, relatively slow inference speeds, and data privacy concerns when using third-party APIs.
In response to these challenges, Predibase is introducing a mechanism that allows customers to deploy their models in a virtual private cloud, ensuring data privacy and providing greater control over the deployment process.
Common mistakes
As more companies venture into machine learning for the first time, Molino shared his insights into some of the most common mistakes they make. He stressed the importance of understanding the data, use case, and business context before diving headlong into development.
A common mistake is having unrealistic expectations and a mismatch between what they expect and what is actually achievable. Some companies transition to machine learning without fully understanding the data or use case, both technically and from a business perspective, says Molino.
Predibase addresses this challenge by providing a platform that facilitates hypothesis testing, integrating data understanding and model training to validate the suitability of models for specific tasks. With guardrails in place, even inexperienced users can engage in machine learning with confidence.
The general availability launch of Predibase’s platform marks an important milestone in their mission to democratize machine learning. By streamlining the development process, Predibase aims to unlock the full potential of machine learning for both organizations and developers.
You can watch our full interview with Molino below:
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California and London. The event takes place in conjunction with the Digital Transformation Week.
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