Generative Data Intelligence

Tag: ML Models

Unlock the potential of generative AI in industrial operations | Amazon Web Services

In the evolving landscape of manufacturing, the transformative power of AI and machine learning (ML) is evident, driving a digital revolution that streamlines operations...

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Best practices to build generative AI applications on AWS | Amazon Web Services

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However,...

Gemma is now available in Amazon SageMaker JumpStart  | Amazon Web Services

Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on...

It’s 10 p.m. Do You Know Where Your AI Models Are Tonight?

If you thought the software supply chain security problem was difficult enough today, buckle up. The explosive growth in artificial intelligence (AI) use is...

Automate Amazon SageMaker Pipelines DAG creation | Amazon Web Services

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we...

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation | Amazon Web Services

AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres...

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker | Amazon Web Services

This is a guest post written by Axfood AB.  In this post, we share how Axfood, a...

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...

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 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...

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