How Google Uses Keras

Google has officially launched LiteRT, the successor to TFLite, which offers significantly faster GPU and NPU acceleration alongside seamless support for PyTorch and JAX. The update also introduces lower-precision data type support for increased efficiency and a commitment to more frequent security and dependency updates across the TensorFlow ecosystem. This transition solidifies LiteRT as Google's primary high-performance framework for deploying GenAI and advanced on-device inference.

The article introduces Gemma 3 270M, a compact AI model designed for hyper-efficient task-specific fine-tuning.

The article discusses how to use KerasHub for loading model weights from SafeTensors into Keras, enabling flexible end-to-end machine learning workflows across different frameworks like JAX, PyTorc...

The article introduces Keras Recommenders, a new library designed to simplify the creation of state-of-the-art recommendation systems using Keras with JAX, TensorFlow, or PyTorch.

The article discusses the launch of ShieldGemma 2, a safety content classifier model built on Gemma 3, aimed at detecting harmful content in both synthetic and natural images.

PaliGemma 2 mix is an advanced vision-language model designed for multiple tasks, allowing developers to utilize a single model for various applications such as image captioning, object detection, ...

PaliGemma 2 is the latest vision-language model from Google, designed to simplify the process of building advanced AI that can interpret visual inputs.

The article announces that Francois Chollet, the creator of Keras, is leaving Google to pursue new opportunities.

The article discusses the expansion of the Responsible Generative AI Toolkit, introducing new tools designed for various large language models (LLMs) like Gemma and Gemini.

The article introduces Keras Hub, a unified library for pretrained models that simplifies access to both natural language processing (NLP) and computer vision (CV) architectures.

The article discusses key takeaways from Season 3 of the 'People of AI' podcast, highlighting themes such as democratizing AI, addressing real-world challenges, and the evolving definition of intel...

The article discusses the PaliGemma architecture, a lightweight open vision-language model (VLM) inspired by PaLI-3.

LiteRT, formerly known as TensorFlow Lite, is a high-performance runtime for on-device AI that now supports models from multiple frameworks including PyTorch, JAX, and Keras.

The article discusses the release of Gemma 2, a new suite of open models that sets a new standard for performance and accessibility in conversational AI.

The article discusses the implementation of Gemma 2, a lightweight large language model (LLM) by Google, for processing streaming data with Dataflow.

The article provides an overview of the Gemma model family architectures, detailing its lightweight, state-of-the-art open models derived from Gemini research.

This article provides a comprehensive guide on building a text-based adventure game using the Gemma 2 model, an instruction-tuned AI model.

The article discusses the advancements in responsible AI through the introduction of Gemma 2, which includes models with 27 billion and 9 billion parameters, emphasizing safety and accessibility.

The article presents three engaging project ideas utilizing Gemma, a family of open models for AI tasks including text generation and code completion.

The article discusses the release of the Gemma 2 model with 27 billion parameters, highlighting its capabilities in Keras and integration with JAX for efficient model training.

Google AI Edge Torch provides a seamless integration from PyTorch to TensorFlow Lite (TFLite), enhancing model coverage and CPU performance for mobile devices.

The article recaps the Google I/O 2024 event, highlighting advancements in AI technologies aimed at making AI accessible for developers.

The article introduces PaliGemma, an open vision-language model, along with Gemma 2, the next generation of the Gemma models, and updates to the Responsible AI Toolkit.

This article discusses how to publish Keras models on Kaggle and Hugging Face, highlighting the ease of sharing fine-tuned models with the community.

The article reveals the program lineup for Google I/O, highlighting key sessions and events for developers.

The article introduces the expansion of the Gemma family with two new models, CodeGemma and RecurrentGemma, designed specifically for developers and researchers.

The article introduces Gemma models in Keras, a family of lightweight, state-of-the-art open models that leverage the same technology as the Gemini models.

Season 3 of the People of AI podcast features a lineup of inspiring guests discussing their journeys in the field of Artificial Intelligence.

The Women in Machine Learning Symposium 2023 is set to take place on December 7, 2023, featuring a diverse lineup of female presenters who will discuss advancements in AI/ML and their practical app...

The article highlights the achievements and activities of Google Machine Learning communities in the second quarter of 2023, showcasing various training campaigns, community events, and innovative ...

The article highlights the career journeys of several Google Developer Student Club alumni who transitioned to Google Developer Experts.

The article discusses new resources available for developers to build AI solutions using Google AI.

The article discusses the launch of the PaLM API and MakerSuite, tools designed to simplify the development of generative AI applications.

The article highlights the achievements of Machine Learning Google Developer Experts (GDEs) in Q2 2021, showcasing their contributions to the global ML ecosystem through various events, projects, a...

The article discusses the advancements made by Coral in 2020 to enhance accessibility to edge AI technologies.

The article discusses the application of machine learning to combat the Fall Armyworm (FAW), a pest that has severely impacted maize crops in Africa.