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At DataRobot, we’re dedicated to serving to our prospects maximize the worth they acquire from our AI Platform. Immediately, we’re excited to share that DataRobot has joined the Amazon SageMaker Prepared Program. This designation helps prospects uncover companion software program options which can be validated by Amazon Internet Companies (AWS) Accomplice Options Architects to combine with Amazon SageMaker. Our companion ecosystem is a key driver in making certain buyer success, and partnering with AWS gives prospects with deep integrations that amplify the productiveness of information science groups.
DataRobot and SageMaker create a strong duo to speed up AI adoption
With DataRobot AI Manufacturing, customers can construct their very own SageMaker containers to coach AI fashions and host them as a SageMaker endpoint, leveraging DataRobot MLOps libraries to mechanically gather and monitor inference metrics. Monitoring jobs may be scheduled natively from DataRobot with out the effort of guide pipelines, liberating up knowledge science sources whereas providing customers full observability throughout numerous SageMaker fashions. Along with conventional MLOps actions, DataRobot AI Manufacturing presents out-of-the-box governance finest practices similar to automated mannequin compliance documentation and mannequin versioning so all DataRobot and SageMaker fashions may be ruled centrally.
Collectively, DataRobot and AWS present a seamless integration that matches the environment and permits higher, quicker data-driven choices with confidence. As DataRobot and AWS now turn into much more aligned, the potential to additional leverage the strengths of each platforms with simplified workflows, enhanced scalability and accelerated time-to-market is tremendously thrilling.
We’re thrilled to be a acknowledged Amazon SageMaker Prepared Accomplice, and look ahead to serving to firms obtain their know-how objectives by leveraging AWS. To study extra about DataRobot’s integration with Amazon SageMaker, obtain the whitepaper right here.
Concerning the SageMaker Prepared Program
Becoming a member of the Amazon SageMaker Prepared Program differentiates DatRobot as an AWS Accomplice Community (APN) member with a product that works with Amazon SageMaker and is mostly obtainable for and totally helps AWS prospects. The Amazon SageMaker Prepared program helps prospects rapidly and simply discover AWS Software program Path companion merchandise to assist speed up their machine studying adoption by offering out-of-the-box abstractions for commonest challenges in machine studying (ML) that construct on high of the foundational capabilities Amazon SageMaker gives.
Amazon SageMaker presents a sturdy set of capabilities and AWS Companions add worth to additional increase the capabilities by integrating with their options. By offering prospects a catalog of Software program Path companion options that carry the complexities of machine studying, the Amazon SageMaker Prepared Program will broaden the person base and enhance buyer adoption. Amazon SageMaker Prepared Program members additionally provide AWS prospects Amazon SageMaker-supported merchandise that supply Amazon SageMaker each in Software program Path Accomplice options they already know, or provide merchandise that simplify every step of the ML mannequin constructing. These purposes are validated by AWS Accomplice Options Architects to make sure prospects have a constant expertise utilizing the software program.
To assist the seamless integration and deployment of those options, AWS established the AWS Service Prepared Program to assist prospects establish options that assist AWS providers and spend much less time evaluating new instruments, and extra time scaling their use of options that work on AWS. Prospects can overview the Amazon SageMaker Prepared Accomplice product catalog to substantiate their most well-liked vendor options are already built-in with Amazon SageMaker. Prospects also can uncover, browse by class or ML mannequin deployment challenges, and choose companion software program options for his or her particular ML improvement wants.
Concerning the writer
Ksenia Chumachenko is a Vice President of Alliances and Enterprise Improvement at DataRobot. She leads Cloud and Know-how Alliances international workforce, serving to purchasers get worth from AI via a wider Cloud and Information ecosystem.
Ksenia has greater than 20 years of expertise delivering technological options and growing companion ecosystems throughout product startups, ISVs, and system integrators. She has ardour for taking partnerships to the following degree through collaboration, creativity, data-driven strategy, and workforce nurturing with profitable expertise in establishing companion channel and constructing groups in pre- and post-IPO knowledge startups.
Ksenia holds an MBA in World Enterprise and Entrepreneurship from NYU Stern College of Enterprise, and B.S. in Laptop Science and Arithmetic from NYU Courant. In her free time she spends time within the San Francisco Bay Space along with her household; they take pleasure in climbing, cooking and going to cultural occasions collectively.
Chen is Director of Accomplice Information Science at DataRobot, the place he drives product integration, demand technology and buyer adoption via tech alliance and channel service companion ecosystem. He leads joint companion AI options to facilitate worth creation for patrons. Previous to DataRobot, Chen was at IBM main inside AI tasks.
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