• 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

  • Lyft

    Lyft, a leading ride-sharing platform that connects millions of riders with drivers across North America, heavily leverages machine learning to optimize their ride-share network and enhance user experience.

    At Lyft, our teams build complex optimization models and perform extensive hyper-parameter tuning using SageMaker Studio notebooks. With SageMaker AI’s remote IDE connectivity, scientists and ML engineers can now connect their customized Cursor or VS Code environments with SageMaker AI with just one click. This seamless integration between our local development environment and SageMaker AI's managed infrastructure empowers us to leverage our familiar tools while accessing SageMaker's powerful compute resources. The result is a much faster model development workflow and hours saved on operational overhead, allowing us to focus on the most critical machine learning problems.

    Yatsiuk Yaroslav, Staff Software Engineer

  • CyberArk

    With remote connections to SageMaker AI, our data scientists have the flexibility to choose the IDE that makes them most productive. Our teams can leverage their customized local setup while accessing the infrastructure and security controls of SageMaker AI. As a security first company, this is extremely important to us as it ensures sensitive data stays protected, while allowing our teams to securely collaborate and boost productivity.

    Nir Feldman, Senior Vice President of Engineering at CyberArk