Tag: Antje Barth

Today, we are announcing an improved Amazon SageMaker Studio experience! The new SageMaker Studio web-based interface loads faster and provides consistent access to your preferred integrated development environment (IDE) and SageMaker resources and tooling, irrespective of your IDE choice. In addition to JupyterLab and RStudio, SageMaker Studio now includes a fully managed Code Editor based on Code-OSS (Visual Studio Code Open Source). Both Code Editor and JupyterLab can be launched using a flexible workspace. With spaces, you can scale the compute and storage for your IDE up and down as you go, customize runtime environments, and pause-and-resume coding anytime from anywhere. You can spin up multiple such spaces, each configured with a different combination of compute, storage, and runtimes. SageMaker…

I’m happy to share that Amazon SageMaker now comes with an improved model deployment experience to help you deploy traditional machine learning (ML) models and foundation models (FMs) faster. As a data scientist or ML practitioner, you can now use the new ModelBuilder class in the SageMaker Python SDK to package models, perform local inference to validate runtime errors, and deploy to SageMaker from your local IDE or SageMaker Studio notebooks. In SageMaker Studio, new interactive model deployment workflows give you step-by-step guidance on which instance type to choose to find the most optimal endpoint configuration. SageMaker Studio also provides additional interfaces to add models, test inference, and enable auto scaling policies on the deployed endpoints. New tools in SageMaker…

Today, we are announcing new Amazon SageMaker inference capabilities that can help you optimize deployment costs and reduce latency. With the new inference capabilities, you can deploy one or more foundation models (FMs) on the same SageMaker endpoint and control how many accelerators and how much memory is reserved for each FM. This helps to improve resource utilization, reduce model deployment costs on average by 50 percent, and lets you scale endpoints together with your use cases. For each FM, you can define separate scaling policies to adapt to model usage patterns while further optimizing infrastructure costs. In addition, SageMaker actively monitors the instances that are processing inference requests and intelligently routes requests based on which instances are available, helping…

I’m happy to share that Amazon SageMaker Clarify now supports foundation model (FM) evaluation (preview). As a data scientist or machine learning (ML) engineer, you can now use SageMaker Clarify to evaluate, compare, and select FMs in minutes based on metrics such as accuracy, robustness, creativity, factual knowledge, bias, and toxicity. This new capability adds to SageMaker Clarify’s existing ability to detect bias in ML data and models and explain model predictions. The new capability provides both automatic and human-in-the-loop evaluations for large language models (LLMs) anywhere, including LLMs available in SageMaker JumpStart, as well as models trained and hosted outside of AWS. This removes the heavy lifting of finding the right model evaluation tools and integrating them into your…