Tag: ai/ml

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, …

Read more “Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model”

Embeddings represent real-world objects, like entities, text, images, or videos as an array of numbers (a.k.a vectors) that machine learning models can easily process. Embeddings are the building blocks of many ML applications such as semantic search, recommendations, clustering, outlier detection, named entity extraction, and more. Last year, we introduced support for text embeddings in …

Read more “Introducing multimodal and structured data embedding support in BigQuery”

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 …

Read more “Build an active learning pipeline for automatic annotation of images with AWS services”

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. …

Read more “Performance deep dive of Gemma on Google Cloud”

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 …

Read more “User-Centered Machine Learning for Visual Search”

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 …

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