There isn’t a boardroom today that isn’t asking what AI and generative AI in application can help drive efficiency and accelerate their business. For organizations looking to capitalize on ML and automation to improve their efficiency during incidents, AIOps is a tangible, proven application thatproves to be an exciting opportunity for ITOps teams. As we’ve seen across market landscape evaluations, there are a number of ways that solutions can be implemented.

Even the best software fails sometimes. How quickly those failures get addressed, and how your teammates and customers feel about you after the fact, comes down to how well you communicate with them. Users, customer success managers, Ops team members, IT, security, engineering leadership, even the executive team. Each has a vested interest in resolving engineering incidents quickly. All need to be updated with the right information at the right time.

Curious about continuous deployment vs delivery? We explain what each is, what happens in each step, and their importance in the DevOps lifecycle.

Curious about MTTR? We explain what the mean time to recovery is, why it matters to your development team, and how to reduce it.

Wondering about incident management KPIs? We explain what incident management metrics are, how to track them, and what to do with the information.

Kubernetes, the de-facto standard for container orchestration, supports two deployment options: imperative and declarative. Because they are more conducive to automation, declarative deployments are typically considered better than imperative. A declarative paradigm involves: The issue with the declarative approach is that YAML manifest files are static.