Fully managed MLflow 3.0 now available on Amazon SageMaker AI

Amazon SageMaker now offers fully-managed support for MLflow 3.0 that streamlines AI experimentation and accelerates your GenAI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.

As customers across industries accelerate their generative AI development, they require capabilities to track experiments, observe behavior, and evaluate performance of models and AI applications. Data scientists and developers lack tools for analyzing the performance of models and AI applications from experimentation to production, making it hard to root cause and resolve issues. Teams spend more time integrating tools than improving their models or generative AI applications. With this launch, fully managed MLflow 3.0 on Amazon SageMaker AI enables customers to accelerate generative AI by making it easier to track experiments and monitor performance of models and AI applications using a single tool. Tracing capabilities in fully managed MLflow 3.0 enable customers to record the inputs, outputs, and metadata at every step of a generative AI application, helping developers quickly identify the source of bugs or unexpected behaviors. By maintaining records of each model and application version, fully managed MLflow 3.0 offers traceability to connect AI responses to their source components, allowing developers to quickly trace an issue directly to the specific code, data, or parameters that generated it. This dramatically reduces troubleshooting time and enables teams to focus more on innovation.

Fully managed MLflow 3.0 on Amazon SageMaker AI is now available in all regions where Amazon SageMaker is offered, excluding China Regions and GovCloud (US) Regions.

To learn more about fully managed MLflow 3.0 on Amazon SageMaker AI, visit the Amazon SageMaker developer guide.

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Source: Amazon Web Services

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