DataRobot Unveils Enterprise AI Suite to Accelerate AI Application Development and Business Value

DataRobot's Veeraraghavan Calls New AI Products 'A Turning Point' For Partner Engagements

'It's a huge, huge set of space for the channel to actually bring their unique capabilities,' DataRobot Chief Product Officer Venky Veeraraghavan tells CRN. DataRobot Chief Product Officer Venky Veeraraghavan sees the artificial intelligence applications and platform vendor's recent spate of AI advancements as a way to "deepen" its relationship with partners and the type of AI services they can provide to customers. In an AI era of point applications serving a single purpose and hyperscalers not allowing deep enough customization, the Boston-based vendor is structuring its platform so that partners and customers can layer subject matter expertise onto an AI app, Veeraraghavan told CRN in an interview. "It'll be a turning point for our engagement with partners," Veeraraghavan said. "It's a huge, huge set of space for the channel to actually bring their unique capabilities. They have deep knowledge of oil and gas or marketing or financial planning. And so they can take our general- purpose platform, specialize it and use it as an end solution for the customer that ... has the DataRobot engine inside it. But it's an application and services that the partner is providing to actually get it to solve the end problem." [RELATED: DataRobot Rolls Out New AI App Building Capabilities] When asked to compare his company's AI platform to products by AI leader Microsoft, Veeraraghavan used the analogy of Microsoft as a supermarket while DataRobot is akin to a meal service like HelloFresh. Veeraraghavan worked at Microsoft for about 20 years before leaving in 2021 to join DataRobot. He left Microsoft with the title of vice president of product management for Azure Cognitive Services, according to his LinkedIn account. "They own GitHub. They have a big platform. But because their customers are so broad, they really focus on the ingredients," he said. "They're more of a grocery store, and they have all the ingredients for you to make any meal you want. They want to say no to almost no one. Our approach is much more ... Here's the recipe. Here are the key ingredients. If you want to [add] more spice, that's great. If you don't want onions, that's great. But it really gets to a good starting point." As for easy AI wins DataRobot partners should explore, Veeraraghavan said that SAP partners should look to DataRobot's framework for building AI applications for users of the Germany-based enterprise software giant's portfolio. The two vendors have a close relationship, he said. "How can we build these great apps for Ariba? Or how do you build great apps for Concur? How do you build great apps for SuccessFactors? Those are all things that we'd love to have partners help us with. And that's just one example. It could be for any given industry." Here's more of what Veeraraghavan had to say about his company's platform and the future for enterprise AI.

DataRobot Rolls Out New AI App Building Capabilities

'Customers ... are not looking for a vector database. They're not looking for millions of parameters and billions of parameters. They're looking for a solution to their problem,' DataRobot Chief Product Officer Venky Veeraraghavan said. A new enterprise artificial intelligence suite for composable AI applications and agents, an add-on AI observability feature and enhanced large and unstructured data preparation functionality are some of the latest advancements DataRobot is rolling out to ready partners and customers for the AI era. The Boston-based AI applications and platform provider revealed the innovations in a series of announcements Tuesday as part of an effort to get AI to end business users, instill more confidence in deploying and running AI apps and unlock more return on organizations' AI investments, DataRobot Chief Product Officer Venky Veeraraghavan told CRN in an interview. Making its offerings more modifiable so that solution providers can add in their expertise and build more services on top is an opportunity for the channel, said Veeraraghavan (pictured). "They can take our general purpose platform, specialize it ... and (deliver) an end solution for the customer that has the DataRobot engine inside it, but really it's an application and services that the partner is providing to solve the end problem," he said. "It really opens up a huge, huge market for partners to build with us and actually then go to market themselves." [RELATED: The 20 Hottest AI Software Companies: The 2024 CRN AI 100] Customers are less dazzled by the components of an AI product than the results it has, Veeraraghavan said. "They are not looking for a vector database. They're not looking for millions of parameters and billions of parameters. They're looking for a solution to their problem." The vendor's close relationship with SAP could make its platform a welcome addition to those partners looking to improve apps built for Ariba, Concur and other products within the German-based tech giant's portfolio, he said. DataRobot has about 300 channel partners worldwide, according to CRN's 2024 Channel Chiefs. DataRobot's new enterprise AI suite promises to allow for composable AI apps and agents with pre-built templates for data analysis tools, predictive content creation systems and other AI use cases, according to the vendor. Users can also adjust security, business and implementation logic of the apps. The suite brings a collaborative AI app library for multiple users in a business to work together on new apps and existing ones. A GenAI app workshop in the suite allows for rapid prototyping and production deployment with automated monitoring and scaling, according to DataRobot. GenAI action tracing promises to make identifying and resolving root causes simpler. Users receive out-of-the-box app interface examples from Streamlit, Flask and Slack. They can also use Dash, Shiny, Microsoft Teams and other frameworks for custom interfaces. Users can tune, refine and view apps in real time, conduct red team activities pre-production and instantly push updates, fixes and improvements without user downtime, according to the vendor. The suite has a declarative application programming interface (API) framework for faster app development and quick integration with SAP products through DataRobot's SAP Datasphere connector and SAP AI Core one-click deployment. Along with the enterprise AI suite, DataRobot has more AI tooling for users, including an add-on AI observability feature and one-click compliance documentation. The full observability wrapper promises to safeguard any app with real-time intervention, moderation and governance in two lines of code, according to DataRobot. The wrapper works for OpenAI, Microsoft Azure, Databricks and other products. DataRobot also has a new Google Vertex integration so that DataRobot tools and evaluate and monitor products and services powered by Vertex and Google Gemini models. The one-click AI compliance documentation capabilities covers the European Union's AI Act, New York's Law No. 144 and other international, local, and industry regulations, according to DataRobot. The vendor's automated compliance tests promise to keep users compliant as new regulations and policies come into effect, and DataRobot offers real-time alerts for compliance. DataRobot enhanced its large and unstructured data preparation and handling functionality to better support GenAI and predictive AI development, according to the vendor. The new and enhanced functionality includes the ability to automate data quality assessments, remediation and healing as well as relationship detection. The detection automation capability allows users to identify and join similar features across datasets and find new features. Users can leverage built-in optical character recognition (OCR) to make unstructured documents ready for AI and build vector databases for faster data retrieval. They can also improve response accuracy with indexing embeddings with metadata, semantically chunking text, enabling multiple retrievals for complex queries, rewriting queries based on chat history, and other techniques. The DataRobot updates will also allow users to process data in iterations, avoid overfitting on large datasets, connect to Nvidia's Rapids AI libraries and other tools and bring in Nvidia Nim microservices, according to DataRobot.

DataRobot unveils enterprise AI app and agent development tools - SiliconANGLE

DataRobot unveils enterprise AI app and agent development tools Artificial intelligence startup DataRobot Inc. today announced a suite of enterprise AI tools to help business customers build generative AI apps and agents customized to meet their needs. With the new enterprise AI toolset, DataRobot customers will find everything they need to build and deploy AI apps faster using prebuilt application templates for a wide range of AI use cases, including agentic workflows, data analysis tools and content creation systems. Venky Veeraraghavan, chief product officer of DataRobot, told SiliconANGLE in an interview this release represents a shift in the company's AI vision from acting as a platform to providing enterprise application tooling. Currently, customers face major challenges in integrating AI into existing business workflows, addressing the complexity and reliability of AI and the needs of modern AI teams. "For us to hit that market, we want to make sure there's a way for the AI lifecycle to meet the dev lifecyle," Veeraraghavan explained. To make this possible, DataRobot provided a generative AI application workshop with out-of-the-box examples of for developers so that data could be easily streamed in from data science teams and sources. This means that AI models and sources could be connected easily to user interfaces for Streamlit, Flask and Slack or using bespoke interfaces with frameworks such as Dash and Shiny. The enterprise AI suite also allows teams to use the company's comprehensive stress testing of generative AI applications for quality assurance before pre-production to make sure they meet business requirements. "The ability to rapidly prototype and deploy generative AI applications is becoming a critical differentiator for businesses," said Ritu Jyoti, group vice president of AI and data market research and advisory at International Data Corp. "DataRobot provides developers with the framework and pre-built components needed to bring innovative generative AI solutions to market quickly. The open architecture ensures that AI teams aren't locked in or stagnating." Additionally, the company announced add-on AI observability and compliance documentation for generative AI applications designed to help safeguard with real-time intervention and governance with minimal coding. Compliance teams can automate AI compliance documentation that adheres to international, local and industry regulations with one-click for various models, including the EU AI Act and NYC Law No. 144. The same documentation can be used to stay compliant in real-time testing using guard libraries with alerts and intervention for models on OpenAI, Google LLC's Vertex, Microsoft Corp.'s Azure and Databricks Inc. "And for AI, if you can see the regulations, they deliver a bunch of controls," said Veeraraghavan. "The controls become tests. Those tests are what's run. You take the results and you document them. Now you can clearly express to the to the regulator what you did if you ever get asked. This is what we did." According to a recent DataRobot survey, 45% of AI professional respondents said they had difficulties with the reliability and consistency of their models. This included mature organizations, making it a top challenge, observability and data consistency monitoring in real-time was a significant concern. DataRobot also announced large and unstructured data preparation handling automation tools to help businesses assess data quality, remediate issues and build accurate models at scale. The tools include the methods for building vector databases for advanced embeddings to improve accuracy for large documents, PDFs and scanned images with text. Every part of the new enterprise AI suite, and DataRobot's new AI vision, Veeraraghavan said, addresses a piece of the modern AI team and the challenges that the industry faces when adopting and building AI applications. "I think that modern AI requires a hybrid team: software developers, data scientists and subject matter experts," Veeraraghavan said. "Very broadly being able to have them all collaborate together and actually deliver an application using their own styles of tools is one of the hardest problems people have."

DataRobot launches Enterprise AI Suite to bridge gap between AI development and business value

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As enterprises worldwide pour resources into AI efforts, many struggle to convert their technological investments into measurable business outcomes. That's the challenge that DataRobot is looking to solve with a series of new product updates announced today. DataRobot is not new to the AI space, in fact the company has been in business for 12 years, well before the current generative AI boom. A core focus for the company since inception has been enabling predictive analytics to help improve business outcomes. Like many others in recent years, DataRobot has turned its attention to gen AI support. With the new Enterprise AI Suite, announced today, DataRobot is looking to go further and differentiate itself in an increasingly crowded market. The new integrated platform promises to enable enterprises to start solving business problems with AI out-of-the-box, rather than having to piece together multiple services. The platform is designed to work across multiple cloud environments as well as on-premises, giving customers more flexibility. The Enterprise AI Suite is a comprehensive platform that helps enterprises build, deploy and manage both predictive and generative AI applications while ensuring proper governance and safety controls. DataRobot's focus is on creating tangible business value from AI, rather than just providing the technology. "How do you take AI to the next level in terms of value creation? I tell people that customers don't eat models for breakfast," Debanjan Saha, CEO of DataRobot, told VentureBeat. "You need to build applications and agents, and not only that, you have to integrate them into their business fabric in order to create value. That's what this release is all about." Addressing the challenges of enterprise AI implementation According to recent DataRobot research, 90% of AI projects fail to move from prototype to production. "Just training models does not create any enterprise value," Saha said. The new DataRobot Enterprise AI Suite introduces application templates that provide immediate functionality while maintaining customization flexibility. This approach addresses a common market gap between inflexible off-the-shelf AI applications and resource-intensive custom development. Saha explained that the templates are designed to be horizontal, meaning they can be applied across different industries, rather than being vertically-specific. While the templates provide a starting point, enterprises have the ability to customize them to their specific needs. This includes: Changing the data sources, adjusting model parameters, modifying the user interface and integrating the applications with other systems in a technology stack. Unifying predictive and generative AI A key differentiator for DataRobot's platform is its unified approach to both traditional predictive AI and gen AI capabilities. The platform allows organizations to extend foundation models with enterprise data while implementing necessary safety controls. DataRobot's Enterprise AI's suite supports a full Retrieval Augmented Generation (RAG) pipeline to help extend foundation models like Llama 3 and Gemini with enterprise data. One of the new templates combines both technologies for enhanced business outcomes. As a potential use case, Saha said for example an enterprise could use the predictive model to predict which customer is going to churn, when they are going to churn and why they are going to churn. Data from that predictive model can then be used with a gen AI model to create a hyper personalized next best offer email campaign. The DataRobot platform includes built-in safeguards for both predictive and generative models. "These models have all sorts of issues with respect to accuracy, with respect to leaking privacy, or private or secure data," Saha noted. "So there are a whole bunch of guard models that you want to put around them." Advanced Agentic AI brings new reasoning to enterprise use cases Another standout feature in the new DataRobot platform is the integration of AI agent capabilities. The agentic AI approach is designed to help organizations handle complex business queries and workflows. The system employs specialist agents that work together to solve multi-faceted business problems. This approach is particularly valuable for organizations dealing with complex data environments and multiple business systems. "You ask a question to your agentic workflow, it breaks up the questions into a set of more specific questions, and then it routes them to agents which are specialists in various different areas," Saha explained. For instance, a business analyst's question about revenue might be routed to multiple specialized agents - one handling SQL queries, another using Python - before combining results into a comprehensive response. Observability and governance are the keys to enterprise AI success As part of the DataRobot updates the company is also rolling out a new observability stack. The new observability capabilities provide detailed insights into AI system performance, especially for RAG implementations. For example, Saha explained that an organization might have a corpus of enterprise data. The organization is using some kind of chunking and embedding model, mapping it to a vector database and then putting an LLM in front of it. What happens if the responses aren't what the organization expects? That's where observability fits in. The platform offers advanced visualization and analytical tools to diagnose such issues. "We have put together a lot of instrumentation which lets people visually understand, for example, if you have a lot of clustering of data in the vector database, you can get a spurious answer," Saha said. "You would be able to see that, if you see your questions are landing in areas where you don't have enough information." This observability extends to the platform's governance capabilities, with real-time monitoring and intervention features. The system can automatically detect and handle sensitive information, with customizable rules for different scenarios. "We are really excited about what we call AI that makes business sense," Saha said. "DataRobot has always been very good at focusing on creating business value from AI - it's not technology for the sake of technology."