Fine-tune Code Llama on Amazon SageMaker JumpStart
Today, we are excited to announce the capability to fine-tune Code Llama models by Meta using Amazon SageMaker JumpStart. The Code Llama family of large language models (LLMs) is a…
Amazon SageMaker
Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker
This post is co-written with Chaoyang He, Al Nevarez and Salman Avestimehr from FedML. Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the…
Enable data sharing through federated learning: A policy approach for chief digital officers
This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc. In this post, we discuss the value and potential…
Best practices to build generative AI applications on AWS
Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However, adoption of these FMs involves addressing…
Gemma is now available in Amazon SageMaker JumpStart
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 Google’s Gemini models,…
Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points
Advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing the financial industry for use cases such as fraud detection, credit worthiness assessment, and trading strategy optimization. To develop…
Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker
In this post, we demonstrate how to efficiently fine-tune a state-of-the-art protein language model (pLM) to predict protein subcellular localization using Amazon SageMaker. Proteins are the molecular machines of the…
Automate Amazon SageMaker Pipelines DAG creation
Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the…
Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation
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 to organizational compliance and security standards.…
How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker
This is a guest post written by Axfood AB. In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence…