Code Llama 70B is now available in Amazon SageMaker JumpStart

Today, we are excited to announce that Code Llama foundation models, developed by Meta, are Read More →

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Read More →

Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning Read More →

Power neural search with AI/ML connectors in Amazon OpenSearch Service

With the launch of the neural search feature for Amazon OpenSearch Service in OpenSearch 2.9, Read More →

Use mobility data to derive insights using Amazon SageMaker geospatial capabilities

Geospatial data is data about specific locations on the earth’s surface. It can represent a Read More →

Host the Whisper Model on Amazon SageMaker: exploring inference options

OpenAI Whisper is an advanced automatic speech recognition (ASR) model with an MIT license. ASR Read More →

Build financial search applications using the Amazon Bedrock Cohere multilingual embedding model

Enterprises have access to massive amounts of data, much of which is difficult to discover Read More →

Modernizing data science lifecycle management with AWS and Wipro

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS Read More →

Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

Large language model (LLM) training has surged in popularity over the last year with the Read More →

Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon Read More →

Overcoming common contact center challenges with generative AI and Amazon SageMaker Canvas

Great customer experience provides a competitive edge and helps create brand differentiation. As per the Read More →

Use Amazon DocumentDB to build no-code machine learning solutions in Amazon SageMaker Canvas

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Read More →

Boost productivity on Amazon SageMaker Studio: Introducing JupyterLab Spaces and generative AI tools

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for Read More →

Techniques for automatic summarization of documents using language models

Summarization is the technique of condensing sizable information into a compact and meaningful form, and Read More →

How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

This is a guest post co-authored by Nafi Ahmet Turgut, Hasan Burak Yel, and Damla Read More →

Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important Read More →

Evaluate large language models for quality and responsibility

The risks associated with generative AI have been well-publicized. Toxicity, bias, escaped PII, and hallucinations Read More →

Accelerate data preparation for ML in Amazon SageMaker Canvas

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often Read More →

Boost inference performance for LLMs with new Amazon SageMaker containers

Today, Amazon SageMaker launches a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Read More →

Accessibility Dashboard

Accessibility settings have been reset

Help = available voice commands

Hide help = available voice commands

Scroll down = available voice commands

Scroll up = available voice commands

Go to top = available voice commands

Go to bottom = available voice commands

Tab = available voice commands

Tab back = available voice commands

Show numbers = available voice commands

Hide numbers = available voice commands

Clear input = available voice commands

Enter = available voice commands

Reload = available voice commands

Stop = available voice commands

Exit = available voice commands