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Tag: SageMaker

Techniques and approaches for monitoring large language models on AWS | Amazon Web Services

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), improving tasks such as language translation, text summarization, and sentiment analysis....

Streamline diarization using AI as an assistive technology: ZOO Digital’s story | Amazon Web Services

ZOO Digital provides end-to-end localization and media services to adapt original TV and movie content to different languages, regions, and cultures. It makes globalization...

Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints | Amazon Web Services

Amazon SageMaker multi-model endpoints (MMEs) are a fully managed capability of SageMaker inference that allows you to deploy thousands of models on a single...

Code Llama 70B is now available in Amazon SageMaker JumpStart | Amazon Web Services

Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy...

Detect anomalies in manufacturing data using Amazon SageMaker Canvas | Amazon Web Services

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable...

How to Use Amazon SageMaker Canvas to Detect Anomalies in Manufacturing Data | Amazon Web Services

Amazon SageMaker Canvas is a powerful tool offered by Amazon Web Services (AWS) that enables users to easily build, train, and deploy machine learning...

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker | Amazon Web Services

This post is co-written with Santosh Waddi and Nanda Kishore Thatikonda from BigBasket. BigBasket is India’s largest...

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access | Amazon Web Services

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs...

How Booking.com modernized its ML experimentation framework with Amazon SageMaker | Amazon Web Services

This post is co-written with Kostia Kofman and Jenny Tokar from Booking.com. As a global leader in...

Automate mortgage document fraud detection using an ML model and business-defined rules with Amazon Fraud Detector: Part 3 | Amazon Web Services

In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at...

Accenture creates a regulatory document authoring solution using AWS generative AI services | Amazon Web Services

This post is co-written with Ilan Geller, Shuyu Yang and Richa Gupta from Accenture. Bringing innovative new...

Announcing support for Llama 2 and Mistral models and streaming responses in Amazon SageMaker Canvas | Amazon Web Services

Launched in 2021, Amazon SageMaker Canvas is a visual, point-and-click service for building and deploying machine learning (ML) models without the need to write...

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