With the recent explosion in machine learning & AI initiatives across all industries, one of the biggest challenges executives face is ensuring project success. Far too often precious resources are wasted on projects that fail to generate value, don’t get off the ground quickly enough, are not adequately resourced, or are quickly rendered obsolete as the business and its customers evolve.
This keynote interview will explore the reasons for these failures and best practices for successfully launching and deploying ML & AI solutions for the enterprise.
Franziska Bell, VP of Data and Analytics at bp
ML projects are expensive. For business leaders considering where the next incremental investment in ML goes, there is a natural tension between investing in front line ML projects and the platforms, tools and teams that can accelerate these projects and help ensure their success. In this session we discuss how to explain the value of ML platforms and infrastructure investments and build the business case for them.
Divya Jain, Director of ML Platform, Adobe
Justin Norman, VP of Data Science, Yelp
Kirk Borne, Principal Data Scientist and Executive Advisor, Booz Allen Hamilton
Recent Gartner research pegged the failure rate of data and AI projects at 60-85%. There are many reasons why AI projects fail, but the question remains: What can leaders do to increase the success rate of ML/AI projects? This panel discussion will explore the principles, processes and practices that allow teams to get more models, with greater impact, launched and into production more quickly and consistently.
Andy Minteer, Senior Director of Emerging Technology, Data Science and IoT, Walmart
Adrian Cartier, VP of Data Science, Ocelot Consulting
Jürgen Weichenberger, Chief Data Scientist, P.E.T. Consulting Inc.
Traditional approaches to managing technical projects, organizing technical teams, and establishing organizational culture can often be at odds with innovating quickly in general and achieving success with machine learning and AI in particular. In this session we discuss how ML/AI executives can build highly effective ML teams, support them with the right processes and tools, and shift the broader organizational culture in ways that support rapid innovation and adoption of machine learning.
Ameen Kazerouni, Chief Analytics Officer, Orangetheory Fitness
Pardis Noorzad, Head of Data Science, Carbon Health
Ziad Asghar, Vice president of Product Management, Qualcomm Technologies
This session will begin with a series of short “conversation starter” talks by accomplished ML/AI leaders. Talks to be followed by a Q&A session and open discussion among Executive Summit participants.
- Laying the foundations of a sophisticated ML org in a greenfield environment – Ameen Kazerouni, Chief Analytics Officer, Orangetheory Fitness
- Demonstrating the value of machine learning: Tracking and attributing model contributions across the enterprise – Paul van der Boor, Senior Director of Data Science, Prosus Group
- Running an AI-First Company – Hussein Mehanna, Vice President and Head of ML/AI, Cruise