Maximize accelerator utilization for model development with new Amazon SageMaker HyperPod task governance | Amazon Web Services
AWS News Blog Maximize accelerator utilization for model development with new Amazon SageMaker HyperPod task governance Today, we’re announcing the general availability of Amazon SageMaker HyperPod task governance, a new innovation to easily and centrally manage and maximize GPU and Trainium utilization across generative AI model development tasks, such as training, fine-tuning, and inference. Cus
Meet your training timelines and budgets with new Amazon SageMaker HyperPod flexible training plans | Amazon Web Services
AWS News Blog Meet your training timelines and budgets with new Amazon SageMaker HyperPod flexible training plans Today, we’re announcing the general availability of Amazon SageMaker HyperPod flexible training plans to help data scientists train large foundation models (FMs) within their timelines and budgets and save them weeks of effort in managing the training process based on compute availabil
Simplify analytics and AI/ML with new Amazon SageMaker Lakehouse | Amazon Web Services
AWS News Blog Simplify analytics and AI/ML with new Amazon SageMaker Lakehouse Today, I’m very excited to announce the general availability of Amazon SageMaker Lakehouse, a capability that unifies data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and artificial intelligence and machine learning (AI/ML) applica
Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart | Amazon Web Services
AWS Machine Learning Blog Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart October 2023: This post was reviewed and updated with support for finetuning. Today, we are excited to announce that Llama 2 foundation models developed by Meta are available for customers through Amazon SageMaker JumpStart to fine-tune and deploy. The Llama 2 family of large language mode
New for Amazon Aurora – Use Machine Learning Directly From Your Databases | Amazon Web Services
AWS News Blog New for Amazon Aurora – Use Machine Learning Directly From Your Databases March 23, 2020: Post updated to clarify networking, IAM permissions, and database configurations required to use machine learning from Aurora databases. A new notebook using SageMaker Autopilot gives a complete example, from the set up of the model to the creation of the SQL function using the endpoint. The int
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AWS announces Amazon SageMaker Partner AI Apps - AWS
Introducing Amazon SageMaker Data and AI Governance - AWS
SageMaker SDK enhances training and inference workflows - AWS
Introducing the next generation of Amazon SageMaker - AWS
AWS expands data connectivity for Amazon SageMaker Lakehouse and AWS Glue - AWS
Announcing Amazon Bedrock IDE in preview as part of Amazon SageMaker Unified Studio - AWS
Announcing the preview of Amazon SageMaker Unified Studio - AWS
Amazon Q Developer is now available in SageMaker Studio - AWS
Llama 2 foundation models from Meta now available in Amazon SageMaker JumpStart
Introducing Amazon SageMaker Serverless Inference (preview)
An Open Source Alternative to AWS SageMaker
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