How To Avoid Building Bad Models

On-demand webinar
DataRobot How To Avoid Building Bad Models Webinar Resource card v.2.0

Don’t be naive when it comes to automated machine learning. Despite the unprecedented speed and ease of creating automated predictive models today, the human mind is still essential for generating good models.

 

From selecting the right problem to solve to preventing algorithm bias, machine learning is still an art and a science. To reap the benefits of automated machine learning, Jen Underwood, Founder of Impact Analytix, will share the most common mistakes – and battle-proven practices –  to help you build better models.

DataRobot How To Avoid Building Bad Models Webinar Resource card v.2.0

On this webinar hosted by DataRobot, you'll learn the secrets to model-building success related to:

  • Selecting the right problem
  • Providing adequate data
  • Properly preparing data
  • Preventing algorithm bias

Speakers

  • 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

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • 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.
    Evariant