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Course
- AI
Monitoring AI in Production
Ensure your AI models stay reliable and effective. This course will teach you how to monitor performance, detect drift, and implement strategies for updates and retraining, giving you the skills to maintain AI systems confidently in production.
What you'll learn
Maintaining AI models in production presents unique challenges, including ensuring consistent performance, adapting to evolving data, and managing operational complexities.
In this course, Monitoring AI in Production, you’ll learn the essential techniques and strategies to keep your AI systems running effectively and reliably. First, you’ll explore the challenges of maintaining AI models in real-world environments, such as data drift, concept drift, and operational bottlenecks. Next, you’ll discover practical techniques for monitoring model performance, including tracking metrics like accuracy, precision, recall, and system resource usage. Finally, you’ll learn how to implement model updates and retraining strategies, incorporating automation, version control, and A/B testing to ensure robust performance. When you’re finished with this course, you’ll have the skills and knowledge to confidently monitor and maintain AI models in production, enabling you to ensure their accuracy, adaptability, and reliability over time.
Table of contents
About the author
Eduardo is a technology enthusiast, software architect and customer success advocate. He's designed enterprise .NET solutions that extract, validate and automate critical business processes such as Accounts Payable and Mailroom solutions. He's a well-known specialist in the Enterprise Content Management market segment, specifically focusing on data capture & extraction and document process automation.
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