Corralling Machine Learning for LCRR Inventory Development
Corralling Machine Learning For LCRR Inventory Development
Watch This Webinar and Earn a PDH
- Understand how to leverage machine learning in inventory development and maintenance
- Learn how to review outputs for accuracy
- Show how the tool works in action
- Review how these models tie into project planning and field work
- Learn proposed guidelines for responsibly using machine learning that regulators can consider
Don't have time to watch the full webinar or want a refresher? Check out this recap article that highlights the main talking points.
Joanna Cummings, PE is the Lead and Copper Rule Compliance Coordinator at CDM Smith. She has over a dozen years of professional experience in the design and optimization of drinking water process systems. She currently is helping utilities in the Midwest and around the country comply with the lead and copper rule revisions, including corrosion optimization studies, and developing lead service line inventories and replacement plans.
Mark Zito, CFM, GISP, is a leadCast product manager for Trinnex. He has 15 years of experience as a Product Leader and Solutions Consultant. He is experienced in project planning, process and requirements analysis, stakeholder engagement and development of workflows that evolve into highly detailed systems. He has spent the past four years immersed in LCRR technology projects.
