Causal AI can help IT and DevOps professionals be more productive, freeing hours of time spent troubleshooting so they can instead focus on building new applications. But when applying Causal AI to IT use cases, there are several domain-specific intricacies that practitioners and developers must be mindful of.
The relationships between application and infrastructure components are complex and constantly evolving, which means relationships and related entities are dynamically changing too. It’s important not to conflate correlation with causation, or to assume that all application issues stem from infrastructure limitations.
In this webinar, Endre Sara defines Causal AI, explains what it means for IT, and talks through specific use cases where it can help IT and DevOps practitioners be more efficient.
We’ll dive into practical implementations, best practices, and lessons learned when applying Causal AI to IT. Viewers will leave with tangible ideas about how Causal AI can help them improve productivity and concrete next steps for getting started.
Tight on time? Check out these highlights
- What is root cause and what is it not? Endre defines what we mean by “root cause” and how to know you’ve correctly identified it.
- How do you install Causely? What resources does it demand? Endre shows how easy it is.