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Tuesday, April 16, 2024

DataRobot Joins the Amazon SageMaker Prepared Program

At DataRobot, we’re dedicated to serving to our clients maximize the worth they achieve from our AI Platform. At present, we’re excited to share that DataRobot has joined the Amazon SageMaker Prepared Program. This designation helps clients uncover accomplice software program options which are validated by Amazon Internet Companies (AWS) Associate Options Architects to combine with Amazon SageMaker. Our accomplice ecosystem is a key driver in guaranteeing buyer success, and partnering with AWS gives clients with deep integrations that amplify the productiveness of information science groups. 

DataRobot and SageMaker create a robust 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 routinely acquire and monitor inference metrics. Monitoring jobs may be scheduled natively from DataRobot with out the trouble of guide pipelines, releasing up information science assets whereas providing customers full observability throughout a lot of SageMaker fashions. Along with conventional MLOps actions, DataRobot AI Manufacturing provides out-of-the-box governance finest practices akin 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, sooner data-driven selections with confidence. As DataRobot and AWS now turn out to be 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.

Bijan Beheshti

International Director, Analytics & Buying and selling, FactSet Analysis Techniques

We’re thrilled to be a acknowledged Amazon SageMaker Prepared Associate, and stay up for serving to firms obtain their expertise targets by leveraging AWS. To study extra about DataRobot’s integration with Amazon SageMaker, obtain the whitepaper right here.

In regards to the SageMaker Prepared Program

Becoming a member of the Amazon SageMaker Prepared Program differentiates DatRobot as an AWS Associate Community (APN) member with a product that works with Amazon SageMaker and is mostly accessible for and totally helps AWS clients. The Amazon SageMaker Prepared program helps clients rapidly and simply discover AWS Software program Path accomplice merchandise to assist speed up their machine studying adoption by offering out-of-the-box abstractions for most typical challenges in machine studying (ML) that construct on prime of the foundational capabilities Amazon SageMaker gives. 

Amazon SageMaker provides a sturdy set of capabilities and AWS Companions add worth to additional broaden the capabilities by integrating with their options. By offering clients a catalog of Software program Path accomplice options that raise the complexities of machine studying, the Amazon SageMaker Prepared Program will broaden the consumer base and improve buyer adoption. Amazon SageMaker Prepared Program members additionally supply AWS clients Amazon SageMaker-supported merchandise that provide Amazon SageMaker each in Software program Path Associate options they already know, or supply merchandise that simplify every step of the ML mannequin constructing. These purposes are validated by AWS Associate Options Architects to make sure clients have a constant expertise utilizing the software program.

To help the seamless integration and deployment of those options, AWS established the AWS Service Prepared Program to assist clients establish options that help AWS companies and spend much less time evaluating new instruments, and extra time scaling their use of options that work on AWS. Prospects can assessment the Amazon SageMaker Prepared Associate product catalog to verify their most popular vendor options are already built-in with Amazon SageMaker. Prospects may uncover, browse by class or ML mannequin deployment challenges, and choose accomplice software program options for his or her particular ML improvement wants. 

White paper

Constructing a Scalable ML Mannequin Monitoring System with DataRobot and AWS

Obtain now

In regards to the writer

Ksenia Chumachenko
Ksenia Chumachenko

VP, Enterprise Growth & Alliances, DataRobot

Ksenia Chumachenko is a Vice President of Alliances and Enterprise Growth at DataRobot. She leads Cloud and Expertise Alliances international workforce, serving to purchasers get worth from AI by a wider Cloud and Knowledge ecosystem.

Ksenia has greater than 20 years of expertise delivering technological options and creating accomplice ecosystems throughout product startups, ISVs, and system integrators. She has ardour for taking partnerships to the subsequent stage by way of collaboration, creativity, data-driven method, and workforce nurturing with profitable expertise in establishing accomplice channel and constructing groups in pre- and post-IPO information startups.

Ksenia holds an MBA in International 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.

Meet Ksenia Chumachenko

Chen Wang
Chen Wang

Channel Knowledge Scientist Director, DataRobot

Chen is Director of Associate Knowledge Science at DataRobot, the place he drives product integration, demand era and buyer adoption by tech alliance and channel service accomplice ecosystem. He leads joint accomplice AI options to facilitate worth creation for purchasers. Previous to DataRobot, Chen was at IBM main inner AI initiatives.

Meet Chen Wang

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