September Machine Learning Updates

Posted by the TensorFlow team

On September 14, at the Google Developers Summit in Shanghai, China, members of Google’s open-source ML teams will be on stage to talk about updates to our growing ecosystem, and we’d love to share them here with you.

MediaPipe Studio

We recognize that creating and productionizing custom on-device ML solutions can be challenging, so we’re reinventing how you develop them by leveraging simple-to-use abstraction APIs and no-code GUIs. We’re excited to give you a sneak peek at MediaPipe Studio, our low-code and no-code solution that gets you from data to modeling to deployment on Android or iOS with native code integration libraries that make it easy to build ML-powered apps.

General Availability of TensorFlow Lite in Google Play Services

We recently launched the general availability of TensorFlow Lite in Google Play services. With this, the TensorFlow Lite runtime is automatically managed and updated by Google Play services, meaning you no longer need to ship it as part of your application. Your apps get smaller, and you get regular updates in the background, so your users will always have the latest version. This is nice for you as an app developer, because your user will get updates and bug fixes to the framework automatically, reducing the burden on you to provide them. And TensorFlow Lite in Google Play Services is production ready, already running over 100 billion daily inferences.

Tensor Projects

At Google, we are creating a world-class family of ML tools across all hardware and device types. Because we are committed to building tools that are fit for purpose, from cutting-edge research to tried-and-true planet-scale deployments, we are sharing our vision of an open ML ecosystem of the future: Tensor Projects.

Tensor Projects is an ecosystem of ML technologies and platforms that bring together Google’s ML tools, and organize efforts across our world-class engineering and research teams. It creates a space and a promise of continued innovation and support to enable researchers, developers, MLOps, and business teams to build responsible and cutting edge ML, from novel model development to scaled production ML in any data center or on any device.

These tools, like TensorFlow, Keras, JAX, and MediaPipe Studio, will work well independently, with each other, and/or with other industry-leading tools and standards. We want to give you full flexibility and choice to build powerful, performant infrastructure for all of your ML use cases. And it’s just the beginning. Tensor Projects will evolve and grow as ML continues to advance. Watch the summary video here:


Updates to

We have an updated experience on for new or advanced users to easily find resources. You can quickly identify the right TensorFlow tool for your task, explore pre-built artifacts for faster model creation, find ideas and inspiration, get involved in the community, discover quick start guides for common scenarios and much more.

PyTorch Foundation

We believe in the power of choice for ML developers and continue to invest resources to make it easy to train, deploy and manage models. Our investment intends to bring machine learning to every developer’s toolbox and covers a broad spectrum of offerings: from TensorFlow and Keras, which provide free and open source offerings to millions of developers, allowing them to succeed with ML, and to JAX, which empowers researchers across Alphabet.

Additionally, in the spirit of openness, we support PyTorch developers with Cloud TPU using XLA. To continue to help all developers succeed with Google Cloud, and to better position Google to make meaningful contributions to the community, we’re delighted to announce our role as a founding member of the newly formed PyTorch Foundation. As a member of the board, we will deepen our open source investment to deliver on the Foundation’s mission to drive the adoption of AI and ML through open source platforms.

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