May 2023

Discover the latest ML innovations from Google I/O

What’s new in machine learning from Google I/O
Discover new tools that make it easy to use the latest ML innovations and build AI applications. Read the recap or watch all of the keynotes and sessions on demand.
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New tools and techniques from TensorFlow and Keras
Explore the latest improvements in the ecosystem to create and deploy powerful, performant models:
Scale your models using parallelism techniques with DTensor
Use state-of-the-art models through KerasNLP and KerasCV
Productionize JAX models with JAX2TF
Read the blog
Train a recommendation model with dynamic embeddings
Learn how dynamic embeddings can be used to dynamically grow and shrink the size of the embedding tables in the recommendation setting. Learn more about building recommendation systems with TensorFlow by attending the Recommendation Systems Dev Summit on June 9.
Read the blog
Join the American Sign Language Fingerspelling Recognition challenge
ASL fingerspelling, which uses hand shapes to represent individual letters, could enable deaf smartphone users to “type” faster than with a virtual keyboard. Join a Kaggle challenge to create a TensorFlow Lite fingerspelling recognition model trained on a new dataset of more than three million characters.
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Explorable: From Confidently Incorrect Models to Humble Ensembles
Learn about Ensembles, a technique that averages the output of multiple models. See how it can be used to improve the quality of a model’s uncertainty estimates.
Read the article
Build generative AI applications with Google’s PaLM 2 model
Large language models can now generate natural language and follow instructions with precision and nuance. Explore new tools that make it fast and easy to use Google’s next-generation models to build innovative AI applications.
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Discover ML resources to build with from Google AI
Introducing a destination for Google AI tools, guidance, and community. Discover resources for creating, training, deploying, and managing ML at scale, no matter where you are in your machine learning workflow or where your models are deployed.
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