All posts by Venky Veeraraghavan

In the years since Gartner last released a Magic Quadrant for Data Science and Machine Learning (DSML), the industry has experienced massive shifts. DataRobot has also transformed dramatically from where we began to where we stand today. The rapid pace of AI advancement is unparalleled, and at DataRobot, I’m most proud of our ability to harness these innovations to ensure organizations can leverage them safely, with governance, and for impactful results. 

This commitment to driving value through AI and our continuous product enhancement is why we are thrilled to be recognized as a Leader in the 2024 Gartner Magic Quadrant for DSML Platforms. Positioned 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 extend my congratulations to the other companies recognized in the Leaders Quadrant—what a recognition!

As one of the industry leaders in this dynamic landscape, this marks the start of a new era for DataRobot. Our journey is defined by ongoing innovation and progression, ensuring that our current offerings are just the beginning of the groundbreaking advancements 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 vendors, typically focusing on vendors in the Leaders quadrant. 

DataRobot is named a Leader in the Magic Quadrant and we also scored the highest for the Governance Use Case in the 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 propelled by our laser focus on reimagining our user experience for both generative and predictive AI, adding full support for code-first AI practitioners, broad ecosystem integration, and reliable multi-cloud SaaS and hybrid cloud support. 

With each launch in Spring ‘23, Summer ’23, and Fall ‘23, we fortified our product offering. As an end-to-end platform, we provide an extensive range of capabilities, enabling us to deliver enterprise-grade AI-driven solutions. This evolution reflects how our hard work has kept pace with the rapid advancements in the generative AI space, as we believe is evidenced by our 4.6 out of 5 score on Gartner Peer Insights based on 538 reviews as of June 27, 2024.

AI-Centric Approach

Our platform is built on a foundation of advanced AI technologies for practitioners and their related stakeholders. Our customers leverage sophisticated machine learning algorithms to analyze extensive datasets, uncovering insights and patterns that drive smart and prompt decision-making. DataRobot complements the platform with forward deployed customer engineering teams and applied AI experts to accelerate value delivery.

Seamless Collaboration

Our goal is to enable synergy among participants throughout the end-to-end DSML lifecycle, addressing the needs of all stakeholders to integrate ML and generative AI into business processes. AI practitioners can share use cases, manage files, and control versions with CodeSpaces, a persistent file system integrated with Git, providing access to our comprehensive, hosted Notebook developer environment anytime, anywhere. 

We ensure rapid deployment of any AI project – whether built on or off the DataRobot platform – to any endpoint or consumption experience, facilitating smooth transitions from AI developers to operators. Our unified approach to generative and predictive AI  development, governance, and operations streamlines activities for data science teams, IT personnel, and business users.

Cross-Environment Visibility

The DataRobot AI Platform offers AI observability across environments, whether cloud or on-premise, for all your predictive and generative AI use cases. The unified view across projects, teams and infrastructure enhance cross-environmental governance and security for all customer AI assets.  

Business Results

Enterprise Strategy Group (ESG) validated DataRobot’s rapid deployment is up to 83% faster compared to existing tools. They also found that it can offer cost savings of up to 80%, with a predicted ROI ranging from 3.5x to 4.6x, providing the necessary analytics capabilities for organizations looking to productionalize 20 models. Having served over 1000 customers, including many of the Fortune 50, DataRobot understands what it takes to build, govern, and operate AI safely and at scale.

Ranked #1 for Governance Use Case

We built our governance capabilities to help our customers establish rigorous 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 have been ranked the highest, with a 4.1 out of 5 governance score from Gartner for Governance Use Case!

Commitment to Continuous Innovation 

Our continuous innovation efforts are evident in the over 80 new features we have released in generative and predictive AI over the last year. We continue to innovate and invest in  the user experience, offering comprehensive support for both highly technical code-first users, and no-code users. Stay tuned to our “What’s New” page to see what we have in store next. We’re already deep into our next groundbreaking release. 

I have been working in the DSML space for over a decade, and I recognize that we are on the cusp 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 both a challenge and pleasure to work in such a dynamic environment where no one knows the “right” answer and we get to test our best ideas and see what works. I look forward to an eventful year or two till the next MQ!

And, if you’re curious about all advancements I talked about, I encourage you all to watch the Data Science and Machine Learning Bake-Off video to see how DataRobot took a problem statement and a raw data set and turned it into an end-user application and judge for yourself.

Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, June 17, 2024.

Gartner Critical Capabilities^TM 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, Published June 24, 2024.

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Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from DataRobot.

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The most inspiring part of my role is traveling around the globe, meeting our customers from every sector and seeing, learning, collaborating with them as they build GenAI solutions and put them into production. It’s thrilling to see our customers actively advancing their GenAI journey. But many in the market are not, and the gap is growing. 

AI leaders are rightfully struggling to move beyond the prototype and experimental stage, it’s our mission to change that. At DataRobot, we call this the “confidence gap”. It’s the trust, safety and accuracy and concerns surrounding GenAI that are holding teams back, and we are committed to addressing it. And, it’s the core focus of our Spring ’24 launch and its groundbreaking features.

This release focuses on the three most significant hurdles to unlocking value with GenAI. 

First, we’re bringing you enterprise-grade open-source LLM support, and a suite of evaluation and testing metrics, to help you and your teams confidently create production-grade AI applications. To help you safeguard your reputation and prevent risk from AI apps running amok, we’re bringing you real-time intervention and moderation for all your GenAI applications. And finally, to ensure your entire fleet of AI assets stay in peak performance, we’re bringing you a first-of-its-kind multi-cloud and hybrid AI Observability to help you fully govern and optimize all of your AI investments.

Confidently Create Production-Grade AI Applications 

There is a lot of talk about fine-tuning an LLM. But, we have seen that the real value lies in fine-tuning your generative AI application. It’s tricky, though. Unlike predictive AI, which has thousands of easily accessible models and common data science metrics to benchmark and assess performance against, generative AI hasn’t—until now. 

Unlike predictive AI, which has thousands of easily accessible models and common data science metrics to benchmark and assess performance against, generative AI hasn’t—until now.

In our Spring ’24 launch, get enterprise-grade support for any open-source LLM. We’ve also introduced an entire set of LLM evaluation, testing, and metrics. Now, you can fine-tune your generative AI application experience, ensuring their reliability and effectiveness.

Enterprise-Grade Open Source LLMs Hosting

Privacy, control, and flexibility remain critical for all organizations regarding LLMs.There has been no easy answer for AI Leaders who have been stuck with having to pick between vendor lock-in risks using major API-based LLMs that could become sub-optimal and expensive in the immediate future, figuring out how to stand up and host your own open source LLM, or custom-building, hosting, and maintaining your own LLM. 

With our Spring Launch, you have access to the broadest selection of LLMs, allowing you to choose the one that aligns with your security requirements and use cases. Not only do you have ready-to-use access to LLMs from leading providers like Amazon, Google, and Microsoft, but you also have the flexibility to host your own custom LLMs. Additionally, our Spring ’24 Launch offers enterprise-level access to open-source LLMs, further expanding your options.

We have made hosting and using open-source foundational models like LLaMa, Falcon, Mistral, and Hugging Face easy with DataRobot’s built-in LLM security and resources. We have eliminated the complex and labor-intensive manual DevOps integrations required and made it as easy as a drop-down selection.

LLM Evaluation, Testing and Assessment Metrics 

With DataRobot, you can freely choose and experiment across LLMs. We also give you advanced experimentation options, such as trying various chunking strategies, embedding methods, and vector databases. With our new LLM evaluation, testing, and assessment metrics, you and your teams now have a clear way of validating the quality of your GenAI application and LLM performance across these experiments. 

With our first-of-its-kind synthetic data generation for prompt-and-answer evaluation, you can quickly and effortlessly create thousands of question-and-answer pairs. This lets you easily see how well your RAG experiment performs and stays true to your vector database.  

We are also giving you an entire set of evaluation metrics. You can benchmark, compare performance, and rank your RAG experiments based on faithfulness, correctness, and other metrics to create high-quality and valuable GenAI applications. 

And with DataRobot, it’s always about choice. You can do all of this as low code or in our fully hosted notebooks, which also have a rich set of new codespace functionality that eliminates infrastructure and resource management and facilitates easy collaboration. 

Observe and Intervene in Real-Time

The biggest concern I hear from AI leaders about generative AI is reputational risk. There are already plenty of news articles about GenAI applications exposing private data and legal courts holding companies accountable for the promises their GenAI applications made. In our Spring ’24 Launch, we’ve addressed this issue head-on. 

With our rich library of customizable guards, workflows, and notifications, you can build a multi-layered defense to detect and prevent unexpected or unwanted behaviors across your entire fleet of GenAI applications in real time. 

Our library of pre-built guards can be fully customized to prevent prompt injections and toxicity, detect PII, mitigate hallucinations, and more. Our moderation guards and real-time intervention can be applied to all of your generative AI applications – even those built outside of DataRobot, giving you peace of mind that your AI assets will perform as intended.

Govern and Optimize Infrastructure Investments

Because of generative AI, the proliferation of new AI tools, projects, and teams working on them has increased exponentially. I often hear about “shadow GenAI” projects and how AI leaders and IT teams struggle to reign it all in. They find it challenging to get a comprehensive view, compounded by complex multi-cloud and hybrid environments. The lack of AI observability opens organizations up to AI misuse and security risks. 

Cross-Environment AI Observability 

We’re here to help you thrive in this new normal where AI exists in multiple environments and locations. With our Spring ’24 Launch, we’re bringing the first-of-its-kind, cross-environment AI observability –  giving you unified security, governance, and visibility across clouds and on-premise environments. 

Your teams get to work in the tools and ways they want; AI leaders get the unified governance, security, and observability they need to protect their organizations. 

Our customized alerts and notification policies integrate with the tools of your choice, from ITSM to Jira and Slack, to help you reduce time-to-detection (TTD) and time-to-resolution (TTR). 

Insights and visuals help your teams see, diagnose, and troubleshoot issues with your AI assets – Trace prompts to the response and content in your vector database with ease, See Generative AI topic drift with multi-language diagnostics, and more.  

NVIDIA and GPU integrations 

And, if you’ve made investments in NVIDIA, we’re the first and only AI platform to have deep integrations across the entire surface area of NVIDIA’s AI Infrastructure – from NIMS, to NeMoGuard models, to their new Triton inference services, all ready for you at the click of a button. No more managing separate installs or integration points, DataRobot makes accessing your GPU investments easy. 

Our Spring ’24 launch is packed with exciting features, including GenAI, predictive capabilities, and enhancements in time series forecasting, multimodal modeling, and data wrangling. 

All of these new features are available in cloud, on-premise, and hybrid environments. So, whether you’re an AI leader or part of an AI team, our Spring ’24 launch sets the foundation for your success. 

This is just the beginning of the innovations we’re bringing you. We have so much more in store for you in the months ahead. Stay tuned as we’re hard at work on the next wave of innovations. 

Get Started 

Learn more about DataRobot’s GenAI solutions and accelerate your journey today. 

• Join our Catalyst program to accelerate your AI adoption and unlock the full potential of GenAI for your organization.
• See DataRobot’s GenAI solutions in action by scheduling a demo tailored to your specific needs and use cases.
• Explore our new features, and connect with your dedicated DataRobot Applied AI Expert to get started with them. 

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