Amazon SageMaker Studio Customers

  • BMW Group

    BMW Group used Amazon SageMaker Studio to build a cost-efficient and scalable ML environment that facilitates seamless collaboration between data science and engineering teams worldwide, allowing ML teams to focus on enabling use cases and accelerating AI innovation.

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  • AstraZeneca

    With SageMaker Studio, AstraZeneca was able to rapidly deploy a solution to analyze large amounts of data, accelerating insights while reducing the manual workload of its data scientists—crucial to AstraZeneca’s mission of discovering and developing life-changing medicines for people around the world.

    Rather than creating many manual processes, we can automate most of the ML development process simply within Amazon SageMaker Studio.

    Cherry Cabading, Global Senior Enterprise Architect – AstraZeneca

  • INVISTA

    INVISTA used Amazon SageMaker Experiments within Studio for model tracking. With an easy interface to manage experiments, get a broader scope of projects, and add new models, metrics, and performance in a structured way, INVISTA accelerated data science value.

    With Amazon SageMaker Studio, we’re now able to co-locate data science tasks. This allows us to save time managing infrastructure and repositories and helps us reduce the time to deploy algorithms and analytics projects into production.

    Tanner Gonzalez, Analytics and Cloud Leader – INVISTA

  • SyntheticGestalt

    With SageMaker Studio and Experiments, SyntheticGestalt can determine the best experiment settings 2x faster, which ultimately accelerates the ability to produce life-changing candidate molecules.

    SageMaker helps our researchers easily compare thousands of experiment settings; they are able to do with a single step what previously consumed hours of our researchers’ time.

    Kotaro Kamiya, CTO – SyntheticGestalt Ltd.

  • MyCase

    Using SageMaker JumpStart within Studio, MyCase launched end-to-end solutions with one click and accessed a collection of notebooks to help them more deeply understand customers and use predictions to better serve their needs.

    Thanks to SageMaker JumpStart, we can deploy a machine learning solution for our own use cases in four to six weeks instead of three to four months.

    Gus Nguyen, Software Engineer, MyCase