Category: FAANG

Posted by Amirkeivan Mohtashami, Research Intern, and Florian Hartmann, Software Engineer, Google Research Large language models (LLMs) have significantly improved the state of the art for solving tasks specified using natural language, often reaching performance close to that of people. As these models increasingly enable assistive agents, it could be beneficial for them to learn …

Read more “Social learning: Collaborative learning with large language models”

Many customers, including those in creative advertising, media and entertainment, ecommerce, and fashion, often need to change the background in a large number of images. Typically, this involves manually editing each image with photo software. This can take a lot of effort, especially for large batches of images. However, Amazon Bedrock and AWS Step Functions …

Read more “Automate the process to change image backgrounds using Amazon Bedrock and AWS Step Functions”

At any given time, eBay’s marketplace has more than 1.9 billion listings from millions of different sellers, running the gamut from the latest gadgets to the most obscure collectibles. Using deep machine learning (ML), AI-driven search capabilities, and natural language processing (NLP), we have streamlined the user experience, making it easier to explore and find …

Read more “eBay accelerates its AI-driven recommendation engine with Vertex AI”

Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use …

Read more “Croissant: a metadata format for ML-ready datasets”

Posted by Omar Benjelloun, Software Engineer, Google Research, and Peter Mattson, Software Engineer, Google Core ML and President, MLCommons Association Machine learning (ML) practitioners looking to reuse existing datasets to train an ML model often spend a lot of time understanding the data, making sense of its organization, or figuring out what subset to use …

Read more “Croissant: a metadata format for ML-ready datasets”

In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. Proteins are the molecular machines of the body, responsible for everything from moving your muscles to responding to infections. Despite this variety, all proteins are made of repeating chains of molecules called …

Read more “Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker”

Kubernetes is a popular way to run AI workloads like training, and large language model (LLM) serving, including our new open model Gemma. Google Kubernetes Engine (GKE) in Autopilot mode provides a fully managed Kubernetes platform that offers the power and flexibility of Kubernetes but without the need to worry about compute nodes, so you …

Read more “Running AI on fully managed GKE, now with new compute options, pricing and resource reservations”