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Course
- AI
AIOps: Observability and Explainability for Production Models
As the world embraces AI, explainability will become essential for maintaining trust. This course will teach you the fundamentals of implementing explainability and observability tools and techniques into your production AI pipelines.
What you'll learn
As more and more organizations embrace AI for increasingly critical decisions, having the ability to observe, assess, and explain why decisions are made at both a macro and micro level will become essential.
In this course, AIOps: Observability and Explainability for Production Models, you’ll learn the tools and techniques that make this possible.
First, you’ll explore why explainability is such a critical capability for a successful model, both in terms of justifying specific decisions as well as demonstrating fairness and lack of bias.
Next, you’ll discover where various observability tools fit in the development lifecycle and operation of a production model.
Finally, you’ll learn how to visualize, share, and operationalize your observability approach.
When you finish this course, you'll have the knowledge necessary to pursue operationalizing your production AI models with greater assurance that your models are fair and accurate and can be explained to any audience.
Table of contents
About the author
Currently an IT leader in Denver Colorado's financial sector Russ has focused on database development, modelling, administration, and BI since 1997 across the Microsoft stack. Russ is a passionate trainer and SQL community volunteer presenting regularly at PASS SQL Saturday events and local user groups around the US.
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