Rare are the data science and engineering teams who are prepared for “Day 2”—the day their models meet the real world. While most teams invest the majority of their time researching, training, and evaluating models, many are unprepared for the challenges that arise in production. The result? A lack of clear processes, tools, and strategies to monitor models effectively once deployed.
This eBook offers a comprehensive, production-first framework for anyone building, testing, or scaling real-time AI systems. Whether you're responsible for AI infrastructure, model reliability, or governance, this guide will help you level up your observability strategy.
Inside, you’ll discover:
- Best practices for maintaining robust model observability in production
- Proven techniques to detect drift, bias, and anomalies before they impact outcomes
- Practical recommendations to reduce alert fatigue and improve operational efficiency
- If you’re deploying models in the real world—or preparing to—this guide is essential reading.
