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Build an active learning pipeline for automatic annotation of images with AWS services

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This blog post is co-written with Caroline Chung from Veoneer. Veoneer is a global automotive electronics company and a world leader in automotive electronic safety systems. They offer best-in-class restraint control systems and have delivered over 1 billion electronic control units and crash sensors to car manufacturers globally. The company continues to build on a …

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Performance deep dive of Gemma on Google Cloud

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Earlier this year we announced Gemma, an open weights model family built to enable developers to rapidly experiment with, adapt, and productionize on Google Cloud. Gemma models can run on your laptop, workstation, or on Google Cloud through either Vertex AI or Google Kubernetes Engine (GKE) using your choice of Cloud GPUs or Cloud TPUs. …

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User-Centered Machine Learning for Visual Search

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AuthorsDimitrios Lymperopoulos, Head of Machine Learning, Palantir Ben Radford, Product Manager, Palantir In our previous blog post, we introduced User-Centered Machine Learning (UCML). The core UCML workflow enables end users to rapidly adapt cutting-edge Computer Vision (CV) capabilities to their specific and evolving missions, based on their feedback. It combines the power of research-grade detection models …

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The Making of VES: the Cosmos Microservice for Netflix Video Encoding

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Liwei Guo, Vinicius Carvalho, Anush Moorthy, Aditya Mavlankar, Lishan Zhu This is the second post in a multi-part series from Netflix. See here for Part 1 which provides an overview of our efforts in rebuilding the Netflix video processing pipeline with microservices. This blog dives into the details of building our Video Encoding Service (VES), and …

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How the Masters uses watsonx to manage its AI lifecycle

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At the Masters®, storied tradition meets state-of-the-art technology. Through a partnership spanning more than 25 years, IBM has helped the Augusta National Golf Club capture, analyze, distribute and use data to bring fans closer to the action, culminating in the AI-powered Masters digital experience and mobile app. Now, whether they’re lining the fairways or watching …

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Knowledge Bases for Amazon Bedrock now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

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With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant, context-specific, and accurate responses without retraining the FMs. In this post, we discuss two new features of Knowledge Bases for …

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