In the years since Gartner last released its Magic Quadrant for Data Science and Machine Learning (DSML), the industry has seen tremendous changes. DataRobot has also transformed dramatically from where we started to where we are today. The rapid pace of AI advancement is unparalleled, and at DataRobot, I’m very proud of our ability to harness these innovations to ensure organizations can leverage them safely, with governance and for effective results.
This commitment to driving value through AI and continuously improving our products is why we are excited to be recognized as a Leader in the 2024 Gartner Magic Quadrant for DSML Platforms. Being placed in the Leaders Quadrant for the first time marks a significant milestone for DataRobot, which we believe reflects our transformation and growing influence in the market. I also congratulate the other companies recognized in the Leaders Quadrant — what recognition!
As one of the industry leaders in this dynamic landscape, this marks the beginning of a new era for DataRobot. Our journey is defined by continuous innovation and advancement, ensuring that our current offerings are just the beginning of the groundbreaking developments on the horizon.
Our journey to the leaders’ quadrant
Gartner evaluates the Magic Quadrant based on a vendor’s ability to execute and completeness of vision. Companies use the Magic Quadrant to shortlist technology suppliers, typically focusing on suppliers in the Leaders quadrant.
DataRobot is called one Leader in the magic quadrant and we noted it too higher for the governance use case in Critical Capabilities for Data Science and Machine Learning Platforms, ML Engineering.
Our journey from democratizing AI to a new set of users, to today expanding to become a unified system of intelligence systems, has been transformative. This journey has been driven by our laser focus on re-engineering the user experience for both AI creation and prediction, adding full source-first support for AI professionals, broad ecosystem integration and reliable multi-cloud SaaS support and hybrid cloud.
With each launch in Spring ’23, Summer ’23 and Fall ’23, we strengthened our product offering. As an end-to-end platform, we provide an extensive range of capabilities, enabling us to deliver AI-powered solutions for businesses. This development reflects how our hard work is keeping pace with rapid developments in the AI manufacturing space, as we believe is evidenced by our 4.6 out of 5 rating on Gartner Peer Insights based on 538 reviews until June 27, 2024.
AI-Centric Approach
Our platform is based on advanced artificial intelligence technologies for professionals and their relevant stakeholders. Our clients leverage sophisticated machine learning algorithms to analyze vast data sets, uncovering insights and patterns that drive intelligent and immediate decision-making. DataRobot complements the platform with front-end customer engineering teams and applied artificial intelligence experts to accelerate value delivery.
Seamless Collaboration
Our goal is to enable synergy between participants throughout the end-to-end DSML lifecycle, meeting the needs of all stakeholders to integrate ML and genetic AI into business processes. AI professionals can share use cases, manage files, and version control with CodeSpaces, a persistent file system built into Git, providing access to our comprehensive, hosted Notebook developer environment anytime, anywhere.
We ensure rapid deployment of any AI project – whether embedded or external to the DataRobot platform – to any endpoint or consumption experience, facilitating a smooth transition from AI developers to operators. Our unified approach to the development, governance, and operations of AI that builds and predicts optimizes operations for data science teams, IT staff, and business users.
Interenvironmental visibility
The DataRobot AI platform offers AI observability across environments, whether in the cloud or on-premises, for all AI use cases that enable you to predict and create. Unified visibility across projects, teams, and infrastructure enhances cross-environment governance and security for all customer AI assets.
Business Results
Validated by the Enterprise Strategy Group (ESG) DataRobot’s rapid deployment is up to 83% faster compared to existing tools. They also found that it can deliver cost savings of up to 80%, with a projected return on investment ranging from 3.5x to 4.6x, providing the necessary analytics capabilities for organizations looking to produce 20 models. Having served more than 1000 customers, including many of the Fortune 50, DataRobot understands what it takes to build, govern and operate AI securely and at scale.
Ranked #1 for Governance Use Case
We built our governance capabilities to help our clients establish strict policies and procedures that protect their bottom line. Our governance framework is designed to uphold the highest standards of integrity, accountability and transparency across all AI operations. We are thrilled to be ranked the highest, with a governance score of 4.1 out of 5 by Gartner for Governance Use Case!
Commitment to Continuous Innovation
Our constant efforts to innovate are evident in the more than 80 new features we’ve released in generative and predictive AI in the last year. We continue to innovate and invest in the user experience, offering comprehensive support for both highly technical users and non-code users. Stay tuned to our ‘What’s New’ page to see what we have in store next. We are already deep into our next groundbreaking release.
I have been working in the DSML space for over a decade and recognize that we are at the threshold of what artificial intelligence has to offer. What I look forward to most every day is listening and learning from our customers and partners to safely accelerate innovation and deliver value. It’s a challenge and a pleasure to work in such a dynamic environment where no one knows the “right” answer and we can try out our best ideas and see what works. Looking forward to an eventful year or two until the next MQ!
And, if you’re curious about all the developments I’ve talked about, I encourage you all to watch it Data Science and Machine Learning Video Bake-off to see how DataRobot took a problem statement and dataset and turned it into an end-user application and judge for yourself.
Gartner, Magic Quadrant for Data Science and Machine Learning Platform, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, June 17, 2024.
Gartner Critical CapabilitiesTM for Data Science and Machine Learning Platforms, Machine Learning (ML) Engineering, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Tong Zhang, Maryam Hassanlou, Raghvender Bhati, Posted on June 24, 2024.
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This chart was published by Gartner, Inc. as part of a larger research paper and should be evaluated in the context of the whole paper. The Gartner document is available upon request from DataRobot.
About the Author
![Venky Veeraraghavan](https://www.datarobot.com/wp-content/uploads/2022/09/venky-1-300x300.jpg)
Venky Veeraraghavan leads the Product Team at DataRobot, where he leads the definition and delivery of DataRobot’s AI platform. Venky has over twenty-five years of experience as a product leader, with previous roles at Microsoft and early stage startup Trilogy. Venky has spent over a decade building BigData and AI platforms for some of the world’s largest and most complex organizations. He lives, hikes and runs in Seattle, Washington with his family.
Meet Venky Veeraraghavan