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On-Demand Webinar

Data Science Fails: Case Studies and Lessons Learned

AI adoption rates are on the rise, and 2020 has become the year that AI becomes business-as-usual rather than a science experiment. But while the adoption of AI has become widespread, not all AI projects are living up to the hype. “Despite the promise of AI, many organizations’ efforts fall short…only 8% of firms engage in core practices that support widespread adoption,” says McKinsey. Great software such as automated machine learning, can help lower the barriers to success, but businesses also need to learn from the mistakes of others.

Join us in this webinar, where we discuss 6 case studies in AI failure, and discover how you can avoid becoming yet another data science failure statistic.

You will learn how to avoid the following traps:

  • Hype surrounding complex new algorithms
  • Perverse incentives
  • Human error
  • Obsolescence in a rapidly changing world
  • Ignoring business rules and human expertise
  • Too much or too little transparency and explainability


Colin Priest
Colin Priest

VP, AI Strategy, DataRobot

  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.