At its I/O developers conference, Google announced the launch of its new ML Hub platform, aimed at providing guidance for developers seeking to train and deploy machine learning models, regardless of their level of expertise. The company aims to make machine learning more accessible and democratized with this new platform. The goal of the platform is to give developers a landing page to look at what kind of model they want to generate, based on the data they have, and then provide step-by-step directions for how to deploy those models.
The ML Hub is launching with an initial set of toolkits that cover a set of common use cases, with plans to update these regularly and launch new ones in a steady cadence. For instance, some of the early toolkits can assist developers in building text classifiers using Keras or taking large language models and running them on Android with Keras and TensorFlow Lite.
According to Alex Spinelli, Google’s VP of product management for machine learning, generative AI may be getting all of the hype at present, but machine learning is a large space that covers a wide range of types of models and technology. He noted that the focus of the platform is on open-source tools, but that the new toolkits will also provide a “glide path into the Google Cloud,” while at the same time remaining open source for use on-premises or in any cloud.
The platform is aimed at a wide audience, from those who are early in their AI career to seasoned professionals. Spinelli emphasized that Google has a sprawling set of open-source technologies that cover many different assets, and the company aims to make it easier to understand how they fit together and help people get up and running.
With the ML Hub, Google aims to cover a wide range of uses of machine learning technology, such as computer vision, facial recognition, recommendation systems, relevance ranking of content, and clustering content. The company has no intention of leaving any area behind and seeks to ensure that developers and researchers have access to the right set of tools and technologies for their particular use case.