The industry has changed dramatically in the years since Gartner last published its Magic Quadrant for Data Science and Machine Learning (DSML). DataRobot has also changed dramatically from where we started to where we are today. The pace of AI advancements is unparalleled, and at DataRobot, I am most proud of our ability to leverage these innovations to help organizations achieve impactful outcomes with safety and governance.
This commitment to value creation through AI and continuous product improvement is why we are delighted to be recognized as a Leader in the 2024 Gartner Magic Quadrant for DSML Platforms. Being recognized in the Leaders Quadrant for the first time is a significant milestone for DataRobot and reflects our transformation and growing influence in the market. We also congratulate the other companies who have been recognized in the Leaders Quadrant. This is truly a great recognition!
As one of the industry leaders in this dynamic environment, this marks the beginning of a new era for DataRobot. Our journey is defined by continuous innovation and progress, ensuring that what we offer today is only the beginning of groundbreaking advancements on the horizon.
Journey to the Leader Quadrant
Gartner evaluates Magic Quadrants based on vendors’ ability to execute and completeness of vision. The company uses Magic Quadrants to identify technology vendors, typically focusing on vendors in the Leaders Quadrant.
DataRobot has been named all We are also leaders in the Magic Quadrant Highest in governance use cases Core competency for data science and machine learning platforms, ML Engineering.
The journey to democratize AI to new user groups and scale to today’s integrated intelligence systems has been transformational. This journey has been driven by reimagining the user experience for generative and predictive AI, focusing on full support for code-first AI practitioners, broad ecosystem integrations, and trusted multi-cloud SaaS and hybrid cloud support.
We have strengthened our product offerings with each release in Spring 2023, Summer 2023, and Fall 2023. As an end-to-end platform, we are able to provide a wide range of capabilities to deliver enterprise-grade AI-based solutions. This evolution demonstrates that our efforts have kept pace with the rapid developments in the field of generative AI. As of June 26, 2024, we believe that our Gartner Peer Insights rating of 4.6 out of 5 stars based on 538 reviews is proof of this.
AI-centric approach
Our platform is built on advanced AI technologies for practitioners and stakeholders. Our customers leverage sophisticated machine learning algorithms to analyze vast data sets and discover insights and patterns that drive smart, fast decision making. DataRobot complements the platform with a forward-deployed customer engineering team and applied AI experts to accelerate value delivery.
Seamless collaboration
Our goal is to enable synergy across participants across the end-to-end DSML lifecycle to meet the needs of all stakeholders and integrate ML and generative AI into business processes. AI practitioners can share use cases, manage files, control versions, and access a comprehensive hosted notebook developer environment anytime, anywhere using CodeSpaces, a persistent file system integrated with Git.
Rapidly deploy any AI project to any endpoint or consumption experience, whether built on the DataRobot platform or not, facilitating a seamless transition from AI developer to operator. A unified approach to generative and predictive AI development, governance, and operations streamlines the activities of data science teams, IT staff, and business users.
Cross-environment visibility
The DataRobot AI Platform provides AI observability across cloud or on-premises environments for all predictive and generative AI use cases. Unified views across projects, teams, and infrastructure enhance cross-environment governance and security for all customer AI assets.
Business Results
Enterprise Strategy Group (ESG) validated that DataRobot’s rapid deployments are up to 83% faster than traditional tools. It also found that it can deliver up to 80% cost savings, delivers predictive ROI of 3.5X to 4.6X, and provides the analytics capabilities organizations need to put 20 models into production. Having served more than 1,000 customers, including many in the Fortune 50, DataRobot understands what it takes to build, manage, and operate AI securely and at scale.
#1 in Governance Use Cases
We have built governance capabilities to enable our customers to establish rigorous policies and procedures that protect their bottom line. Our governance framework is designed to maintain the highest levels of integrity, accountability, and transparency across all AI operations. We are thrilled to be ranked highest by Gartner in the Governance Use Cases category, with a score of 4.1 out of 5!
Commitment to continuous innovation
Our continued commitment to innovation is evident in the over 80 new features we released last year in generative and predictive AI. We continue to innovate and invest in the user experience, providing comprehensive support for both highly technical code-first and no-code users. Keep an eye on our “What’s New” page to see what’s coming next. We’re already deep into our next groundbreaking release.
I have been working in DSML for over 10 years and know that we are at the pinnacle of what AI 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 value delivery. It is challenging and fun to work in a dynamic environment where no one knows the “right” answer and where we test the best ideas and see what works. Looking forward to a year or two full of events until the next MQ!
And if you’re curious about all the advancements I’ve mentioned, I encourage you to watch the Data Science and Machine Learning Bakeoff video, where we show you how DataRobot transforms problem statements and raw data sets into end-user applications, and you can see for yourself.
Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jafri, Aura Poppa, Peter Krensky, Jim Hare, Ragvendar Bhatti, Maryam Hasanlu, Tong Zhang, 17 June 2024.
Gartner Critical CapabilitiesTM Data Science and Machine Learning Platforms, Machine Learning (ML) Engineering, Apraz Jafri, Aura Poppa, Peter Krensky, Jim Hare, Tong Jang, Maryam Hasanlu, Ragvendar Bhatti, published on June 24, 2024.
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About the author
Venky Veeraraghavan leads DataRobot’s product team, driving the definition and delivery of DataRobot’s AI platform. Venky has over 25 years of experience as a product leader, having previously worked at Microsoft and an early-stage startup, Trilogy. For over a decade, Venky has built hyperscale big data and AI platforms for some of the world’s largest and most complex organizations. He lives in Seattle, Washington with his family, and enjoys hiking and running.
Meet Venky Veeraraghavan
