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AWS introduces managed MLflow to Amazon SageMaker – VentureBeat #MLflow

Few-shot tool-use doesn’t really work (yet)

Amazon Web Services (AWS) has announced the integration of managed open source MLflow into Amazon SageMaker, a machine learning service. This collaboration aims to simplify the process of building, training, and deploying machine learning models for developers and data scientists. MLflow, an open source platform, allows users to manage the end-to-end machine learning lifecycle, from experimentation to production deployment. By integrating MLflow into Amazon SageMaker, AWS is providing users with a more streamlined and efficient way to manage their machine learning projects. This partnership will enable users to easily track experiments, reproduce results, and deploy models at scale. Additionally, AWS will provide support and maintenance for the managed MLflow service, ensuring that users have access to the latest features and updates. Overall, this collaboration between AWS and MLflow is expected to enhance the machine learning experience for developers and data scientists by simplifying the process of building and deploying models on Amazon SageMaker.

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