Innovations for Business Value

Debanjan Saha, CEO, declares a new era of AI, one that is defined by proving the measurable business value of AI. He will share a vision for the future of the industry and a roadmap for customers who are looking to derive value, today. He’ll be joined by Marc Neumann, Head of AI Platform, BMW Group, to discuss how high performing companies are rethinking their approach to AI.

The DataRobot AI Platform 9.0 Release

Venky Veeraraghavan, Chief Product Officer, will unveil some of  the game-changing advancements within the DataRobot AI Platform 9.0 release. He’ll be joined by Torsten Grabs, Director of Product Management at Snowflake, to dive deeper into the new Snowflake integration capabilities.

Accelerating Business Impact with DataRobot Applied AI Expertise

Working with our customers, Lisa Aguilar, VP of Field CTOs and Product Marketing, knows that to be successful and derive value with AI,  it takes more than just great technology. It takes a vision, and clear understanding of how to navigate through the stages to scale AI. Join her to learn more about DataRobot’s differentiated Applied AI Expertise.

Continuous innovation creating value for customers

Join DataRobot CTO, Michael Schmidt to preview what’s next for the DataRobot AI Platform, including bringing the best possible models, best practices, and capabilities to help you to realize even more value faster. Dominic Divakaruni, Head of Product, Azure OpenAI Service, will join Michael to talk about the game-changing use cases we can expect to see in the market.

Breakout session 1: How DataRobot Integrates into a Broad Enterprise Technology Ecosystem

Successful AI environments require flexibility and extensibility. 

Learn how DataRobot’s open and complete AI lifecycle platform provides seamless and secure integrations with your existing investments in data platforms, AI frameworks, DevOps tools, application stacks, and business processes.

Breakout session 2: Automate Compliance and Governance in ML Production

As organizations grow and optimize business through AI, there is a pressing need to ensure robust governance of models used in business-critical applications and workflows. For both regulated and unregulated industries, it is crucial to incorporate data science best practices like model documentation, centralized monitoring of models, and tackling bias. Such a framework also enables organizations to meet government regulations and reduce overall risk from models in production.  

Breakout session 3: Generate and Maintain Value of AI at Scale

Many organizations struggle to manage and maintain their growing technology ecosystem while trying to generate more value from their AI initiatives. To systematically realize the value of AI at scale, there is a need for more workflow automation and integration of ML across business functions. Machine learning lifecycles need to be treated similarly to software development lifecycles, with continuous integration and continuous development.

Breakout session 4: Accelerate Experimentation

Machine Learning projects and artifacts are scattered across local and shared systems, making it difficult to rapidly iterate and execute end to end ML projects in a collaborative manner. Further, data scientists need to work closely with business SMEs to discover use cases and show tangible returns using ML for them. To foster this frictionless collaboration for AI teams with multiple stakeholders, it is important to enable organized access to shared resources for a particular business problem. With the needed assets in one place, data scientists have faster iteration between data prep and modeling and more opportunities for collaboration by inviting other data scientists to engage in the use case and be instantly familiarized with the ML project.