Your AI and ML projects are failing – Key steps to get them back on track

Oct 18th, 2019

Gary Allemann, MD at Master Data Management

With recent studies indicating that 80% of Artificial Intelligence and Machine Learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact.

This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.

Join Master Data Management for this Syncsort webinar. Taking place on Tuesday, the 22nd of October 2019, the webinar will start with four foundational data steps to get AI and ML projects grounded and underway, specifically:

  • Framing the business problem.
  • Identifying the “right” data to collect and work with.
  • Establishing baselines of data quality through data profiling and business rules.
  • Assessing fitness for purpose for training and evaluating the subsequent models and algorithms.

To register for this webinar click here.