For many companies, the notion that Machine Learning and Artificial Intelligence can drive better business outcomes has been widely accepted, but the practicality of going from concept to operational use in decision-making has presented many challenges.

For business stakeholders, understanding what must happen for your business case to be successfully translated into an end-to-end machine learning project can help create better communication, expectations, and join up throughout projects. In this recorded webcast Thorogood Data and Analytics Consultant Amanda Teschko shares frameworks, definitions, and approaches that can help business stakeholders, data scientists, and data engineers communicate and organize more effectively to drive greater success on end-to-end, business-value-driven machine learning projects.

By taking an end-to-end view for ML implementations, our frameworks can help distill a high value business case into practical requirements from architectural configurations, data inputs and engineering, hypothesis definition, model development, model serving, monitoring/maintenance, and business user adoption.