Video

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.

In this session you will learn:

  • How to speed and scale experimentation with new DataRobot capabilities, including both code-first and GUI based options
  • Why a collaborative experimentation experience is critical for today’s data-driven enterprises
  • How global motorsports leader Polaris amplified the productivity of the data science team

Speakers

Jillian Schwiep
Jillian Schwiep

Product Manager, DataRobot

Luke Bunge
Luke Bunge

Data Science Product Manager, Polaris Inc.

  • 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.
    Evariant