AI and Machine Learning @ I/O Recap

Posted by TensorFlow Team

Google I/O 2022 was a major milestone in the evolution of AI and Machine Learning for developers. We’re really excited about the potential for developers using our technologies and Machine Learning to build intelligent solutions, and we believe that 2022 is the year when AI and ML become part of every developer’s toolbox.

At the I/O keynotes we showed our fully open source ecosystem that takes you from end to end with Machine Learning. There are developer tools for managing data, training your models, and deploying them to a variety of surfaces from global scale cloud all the way down to tiny microcontrollers…and of course ways to monitor and maintain your systems with MLOps. All of this comes with a common set of accelerated hardware for training and inference, along with open source tooling for responsible AI end-to-end.

You can get a tour through this ecosystem in the Keynote “AI and Machine Learning updates for Developers”

Responsible AI review processes: From a developer’s point of view

We can all agree that responsible and ethical AI is important, but when you want to build responsibly, you need tooling. We could, and will, create a whole video series about these tools, but the great content to watch right now is the talk on the Responsible AI review process. Googlers who worked on projects like the Covid-19 public forecasts or the Celebrity Recognition APIs will take you step-by-step through their thought process and how the tools lined up to help them build more responsibly and thoughtfully. You’ll also learn about some of the new releases in Responsible AI tools, such as the to all AI/ML talks and workshops.

That’s it for this roundup of AI and ML at Google I/O 2022. We hope you’ve enjoyed it, and we’d love to hear your feedback when you explore the content. Please drop by the TensorFlow Forum and let us know what you think!