Accelerate AI development using Amazon SageMaker AI with serverless MLflow
Since weannounced Amazon SageMaker AI with MLflow in June 2024, our customers have been using MLflow tracking servers to manage theirmachine learning (ML)and AI experimentation workflows. Building on this foundation, we’re continuing to evolve the MLflow experience to make experimentation even more accessible.
Since weannounced Amazon SageMaker AI with MLflow in June 2024, our customers have been using MLflow tracking servers to manage theirmachine learning (ML)and AI experimentation workflows. Building on this foundation, we’re continuing to evolve the MLflow experience to make experimentation even more accessible.
Today, I’m excited to announce thatAmazon SageMaker AI with MLflownow includes a serverless capability that eliminates infrastructure management. This new MLflow capability transforms experiment tracking into an immediate, on-demand experience with automatic scaling that removes the need for capacity planning.
The shift to zero-infrastructure management fundamentally changes how teams approach AI experimentation—ideas can be tested immediately without infrastructure planning, enabling more iterative and exploratory development workflows.
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