Amazon SageMaker AI now offers a serverless MLflow capability that dynamically scales to support AI model development tasks. With MLflow, AI developers can begin tracking, comparing, and evaluating experiments without waiting for infrastructure setup.
As customers across industries accelerate AI development, they require capabilities to track experiments, observe behavior, and evaluate the performance of AI models, applications and agents. However, managing MLflow infrastructure requires administrators to continuously maintain and scale tracking servers, make complex capacity planning decisions, and deploy separate instances for data isolation. This infrastructure burden diverts resources away from core AI development and creates bottlenecks that impact team productivity and cost effectiveness.
With this update, MLflow now scales dynamically to deliver fast performance for demanding and unpredictable model development tasks, then scales down during idle time. Administrators can also enhance productivity by setting up cross-account access via Resource Access Manager (RAM) to simplify collaboration across organizational boundaries.
The serverless MLflow capability on Amazon SageMaker AI is offered at no additional charge and works natively with familiar Amazon SageMaker AI model development capabilities like SageMaker AI JumpStart, SageMaker Model Registry and SageMaker Pipelines. Customers can access the latest version of MLflow on Amazon SageMaker AI with automatic version updates.
Amazon SageMaker AI with MLflow is now available in select AWS Regions. To learn more, see the Amazon SageMaker AI user guide and the AWS News Blog.
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Source: Amazon Web Services
