How Google Uses Mistral

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

The article discusses the integration of Google’s Agent Development Kit (ADK) for Java with the LangChain4j LLM framework, enabling developers to utilize a variety of Large Language Models (LLMs) f...

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 the Agent Development Kit (ADK), an open-source framework by Google designed to facilitate the development of multi-agent applications.

Genkit for Go is an open-source framework designed to help developers build scalable AI-powered applications using the Go programming language.

The ML Olympiad 2024 is a series of globally distributed machine learning competitions organized by the Google ML Community, providing developers opportunities to learn and practice machine learnin...

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.