Fundamentals for Efficient ML Monitoring

Rare are the data science and engineering teams who are prepared for “Day 2” , the day their models meet the real world; as they invest the majority of their time researching, training, and evaluating models. While it’s clear that teams want to address any potential issues before they arise, there is a lack of clear processes, tools and requirements for production systems.

This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere. You will learn:
  • Best practices for monitoring your models in production
  • Proven ways to catch drifts, biases and anomalies at the right time
  • Recommendations to avoid alert fatigue

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