10 Measures and KPIs for ML Success
Event submitted on Monday, April 27th 2020, approved by Charles Villanueva ✓
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This is one of several events being organized by Ai4 within the AI (Artificial Intelligence) space. If you’re working in the space then these are really “must attend” events – all taking place online. This one focuses on Machine Learning and how to set KPI’s.
The following description was either submitted by the Conference Organizer on Monday, April 27th 2020, or created by us.
Evaluating the impact that machine learning operations (MLOps) will have on the business is an essential part of any AI/ML business case. The business is looking to us AI/ML as a competitive advantage but often stops short at looking only at the Data Science Investment. As an MLOps professional there are critical outcomes you can measure and KPIs to track that will help you justify the investment in platforms and applications to support ML at scale.
Use these measures and KPIs to justify investment in an ML-focused lifecycle required to build, deploy, and operate enterprise-ready models that generate real value.
This webinar will discuss:
- 5 areas where MLOps has a measurable impact
- 5 best practices KPIs to monitor to demonstrate value
- Real-world examples of how organizations have made this pay off for their business