Generative Data Intelligence

Tag: Container registry

Build a medical imaging AI inference pipeline with MONAI Deploy on AWS | Amazon Web Services

This post is cowritten with Ming (Melvin) Qin, David Bericat and Brad Genereaux from NVIDIA. Medical imaging AI researchers and developers need a scalable,...

Dialogue-guided visual language processing with Amazon SageMaker JumpStart | Amazon Web Services

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing....

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker | Amazon Web Services

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative...

How Meesho built a generalized feed ranker using Amazon SageMaker inference | Amazon Web Services

This is a guest post co-written by Rama Badrinath, Divay Jindal and Utkarsh Agrawal at Meesho. Meesho is India’s fastest growing ecommerce company...

Build and deploy ML inference applications from scratch using Amazon SageMaker | Amazon Web Services

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business...

Train and deploy ML models in a multicloud environment using Amazon SageMaker | Amazon Web Services

As customers accelerate their migrations to the cloud and transform their business, some find themselves in situations where they have to manage IT operations...

How VirtuSwap accelerates their pandas-based trading simulations with an Amazon SageMaker Studio custom container and AWS GPU instances | Amazon Web Services

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange...

Implement smart document search index with Amazon Textract and Amazon OpenSearch | Amazon Web Services

For modern companies that deal with enormous volumes of documents such as contracts, invoices, resumes, and reports, efficiently processing and retrieving pertinent data is...

Run multiple generative AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe and save up to 75% in inference costs | Amazon...

Multi-model endpoints (MMEs) are a powerful feature of Amazon SageMaker designed to simplify the deployment and operation of machine learning (ML) models. With MMEs,...

Automatically generate impressions from findings in radiology reports using generative AI on AWS | Amazon Web Services

Radiology reports are comprehensive, lengthy documents that describe and interpret the results of a radiological imaging examination. In a typical workflow, the radiologist supervises,...

Zero-shot text classification with Amazon SageMaker JumpStart | Amazon Web Services

Natural language processing (NLP) is the field in machine learning (ML) concerned with giving computers the ability to understand text and spoken words in...

Deploy thousands of model ensembles with Amazon SageMaker multi-model endpoints on GPU to minimize your hosting costs | Amazon Web Services

Artificial intelligence (AI) adoption is accelerating across industries and use cases. Recent scientific breakthroughs in deep learning (DL), large language models (LLMs), and generative...

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