We continue to grow the family of products and open source services that make up the Google AI/ML ecosystem. In recent years, we learned that a single universal framework could not work for all scenarios – in particular, the needs of production and cutting edge research are often in conflict. So we created JAX, a minimalistic API for distributed numerical computing to power the next era of scientific computing research. JAX is excellent for pushing new frontiers: reaching new scales of parallelism, advancing new algorithms and architectures, and developing new compilers and systems. The adoption of JAX by researchers has been exciting, and advances such as AlphaFold and Imagen underscore this.

In this new multi-framework world, TensorFlow is our answer to the needs of applied ML developers – engineers who need to build and deploy reliable, stable, performant ML systems, at any scale, and for any platform. Our vision is to create a cohesive ecosystem where researchers and engineers can leverage components that work together regardless of the framework where they originated. We've already made strides towards JAX and TensorFlow interoperability, in particular via jax2tf. Researchers who develop JAX models will be able to bring them to production via the tools of the TensorFlow platform.

Going forward, we intend to continue to develop TensorFlow as the best-in-class platform for applied ML, side-by-side with JAX to push the boundaries of ML research. We will continue to invest in both ML frameworks to drive forward research and applications for our millions of users.

There’s lots of great stuff baking that we can’t wait to share with you, so watch this blog for more details!

PS: Interested in working on any of our AI and ML frameworks? We're hiring.