ML monitoring that gets to the root of the problem.

An introduction to TruEra Monitoring

Tired of wild goose chases?There’s got to be a better way to supervise and debug machine learning models in production. (Hint: there is.)

Effective ML model monitoring is a key part of AI Quality management. Monitoring ensures that your machine learning models are effective and delivering value to your business. The problem? Most model monitoring solutions today are inaccurate and ineffective, leading to too much wasted time and effort. Data scientists and MLOps teams deserve better.

Join us as TruEra’s Head of Product, Justin Lawyer, and sales engineer Colin Goyette give you an overview of a better approach: TruEra Monitoring.

During the 40-minute webinar, we will cover:

  • Why ML model monitoring is critical to business success
  • The problems with ML monitoring today - alert fatigue, wild goose chases
  • TruEra Monitoring - getting to the heart of the problem, fast
  • Demo of TruEra Monitoring


Watch the webinar now

Meet the Speakers



Justin Lawyer, Head of Product at TruEra

Justin is a 20-year machine learning and product veteran and brings a wealth of practical AI, commerce, and risk experience to TruEra. Previously, Justin spent 14+ years at Google where he ran their Cloud AI Platform team and built Google’s machine learning platform for payment fraud and abuse prevention.

Colin Goyette, Customer-Facing Data Scientist at TruEra

Colin is an experienced data scientist who enjoys helping enterprises with their machine learning initiatives. He has a Masters degree in Artificial Intelligence from Columbia University and has held data science and engineering roles at Domino Labs, IBM, and Dell. 

About TruEra

TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.