Monitoring and AIOps Make the Best DevOps Platform

When it comes to delivering software through the DevOps model, the priority of the platform is increasingly clear. DevOps platforms are multi-tenant, self-service, developer-focused, and an essential component of a multi-cloud strategy. They provide standard guide rails, tools, and techniques for developers to easily build, test, and iterate. However, the key element that should not be neglected when running a DevOps model is flexibility.

DevOps breaks down homogeneous products into smaller value streams that can be delivered as independent cloud-based services. Once teams are prepared for delivery under this model, they will be formalized through Service Level Agreements (SLAs). To achieve this, strong monitoring and warning practices must be put in place. As with any DevOps practice, automation is the end goal – and when it comes to monitoring and alerting, the AIOps platform is the gold standard.

platform approach

Without the AIOps platform, event volumes and alerts can quickly spiral out of control. There is also the problem of determining how alerts from different systems relate, especially if those systems and their teams are isolated. Crucially, there is no internal intelligence to help identify and predict problems before they become critical, and thus advanced tools and techniques, such as machine learning, cannot be harnessed for self-healing.

To ensure that the AIOps platform can be designed effectively, an advanced understanding of the monitoring data must first be established. For DevOps engineers, aiming to implement AIOps capabilities, creating a monitoring platform that enables alerts to be prioritized and then fed into advanced processing tools is a must. Of course, P1 (Priority 1) incidents will always require an immediate response, but less priority incidents are often viewed by customers as a priority. Likewise, a set of less significant events across systems may lead to more severe accidents. To understand and respond to these requests, and to link alerts across systems, a robust monitoring system needs to be in place.

The context of incidents is essential if treatment – and the rules governing the automation of this process – are to be effective. It should also be remembered that more advanced monitoring will provide more alerts, so the ability to scale the monitoring will become essential. This is where cloud-native DevOps platforms offer great value, as they provide the means to manage rapidly increasing volumes of data.

Shift left and right

To move towards the AIOps model, a set of practices and tools must be implemented between shifting left and right. This means prioritizing monitoring early in development while continuously incorporating feedback from production. Once monitoring and alerts are managed at scale under this model, machine learning and other advanced analytics techniques can be harnessed through the AIOps platform to automate these processes, resulting in more proactive, detailed, and dynamic insights and processing. Ultimately, this results in organizations achieving greater flexibility by ensuring that service level objectives are met, delivery is improved, and customer satisfaction is increased.

Without the AIOps platform, remediation will require specialized experts (SMEs) across different domains, from cloud infrastructure to application architecture, to meet in order to Determine the root cause of the accident, which becomes a drain on time and resources. The AIOps platform can ensure automatic participation of relevant SMEs by alerting them as soon as a P1 incident occurs, resulting in disruption reduction and targeted treatment.

Improve developer and customer experience

AIOps are necessary if service providers want to create an advanced DevOps mode. It enables developers to feed into a secure CI/CD pipeline, thereby driving changes into production with confidence, as quality assurance is automated – enhancing the right transformation capabilities of an organization. While this, of course, reduces the burden on developers to create quality portals and reduces requirements around peer reviews, the model also provides increased customer satisfaction, as applications and features can be updated securely and frequently, while maintaining and improving service availability.

Search Show that the majority of incidents (74%) are discovered by customers before support teams realize there is a problem. When we consider that 66% of current monitoring solutions only identify less than half of all performance issues or outages, and that the increasing complexity of IT—typically driven by rapid adoption of the cloud—is leading to more outages, it is clear that a move to smarter solutions is required. . Customers today not only expect service providers to maintain service availability at 99.99% and 99.999%, they also require a view of service performance.

The monitoring platform is able to provide this insight through advanced reporting and data visualization tools, allowing multi-purpose dashboards to be easily created. This data can then be used by DevOps engineers to create self-repairing manuals that can be integrated into the AIOps platform – again to improve the developer experience.

The ultimate goal of DevOps engineers when designing the DevOps platform is to create an environment that feels as if it was made for the developer, by the developer. Reducing the time developers spend implementing various capabilities, such as security features, testing, and monitoring, allows them to focus on improving their service delivery, creating an optimal experience for both developer and customer. The introduction of automation into the processing process through the AIOps platform enhances this improvement as the chances of potential production breaks are significantly reduced. This is the model that all service providers aim at with their DevOps strategy.

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