AIOps enhances container lifecycle management by driving a new level of automated orchestration of Kubernetes. Handle application configuration, container observability, control Kubernetes Operators, and enable automated lifecycle management of the entire application stack in Kubernetes with AIOps. Combined with the power of anomaly detection, AIOps drives safer, low-touch container operations.
Kubernetes deployments are complex and bring in many challenges in configurations, security, and difficulty of migrating legacy applications. This requires experts who are comfortable with Kubernetes deployments and also with the enterprise application stack. By using AIOps, enterprises can skip past these dependencies to automate management and monitoring of container deployment.
Issues with container deployments are complex and require understanding of the impact of dependencies between containers as well as deep knowledge of container management systems like Kubernetes. AI models can replicate the actions of experts and automatically handle all routine issues and flag unknown issues for human supervision. Model actions can control elements like Kubernetes Operators so that all container management activities can be done by smaller teams without wasting dedicated resource time on these activities.
Scaling up container adoption requires two things - migrating legacy applications to containers and replicating container workloads onto multi-cloud systems or edge nodes. Consequently, this increases the scale of monitoring metrics, managing configurations, and handling workloads across many container deployments. By automating all management and workload configuration, AIOps lets the experts work on complex issues like migrating legacy applications and allocating deployments by usage of services.
Monitoring technical performance of container deployments is simple for on-premise and single cloud deployments. However, managing multi-cloud environments and edge nodes dramatically increases the monitoring needs. Moreover, it's not a simple task to connect technical performance with business results. By using AIOps for governance, enterprises can use a single platform to monitor their entire container workloads and correlate them to business results. In addition, they can also contrast them against the performance of VM and cloud deployments to capture the RoI of container adoption.