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.
