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
