How Monitoring and AIOps Delivers the Ultimate DevOps Platform

When it comes to delivering software through a DevOps model, the primacy of the platform is increasingly evident. DevOps platforms are multi-tenant, self-service oriented, developer-centric, and are an essential component of a multi-cloud strategy. They provide guide rails and standardized tools and technologies for developers to build, test, and iterate with ease. A core component that must not be neglected when operating a DevOps model, however, is resilience.  

DevOps breaks down monolithic products into smaller value streams that can be delivered as independent cloud-based services. Once teams are set up to deliver under this model, it will be formalized through service level agreements (SLAs). To deliver against these, robust monitoring and alerting practices must be put in place. As with any DevOps practice, automation is the ultimate goal — and when it comes to monitoring and alerting, an AIOps platform is the gold standard. 

The Platform Approach 

Without an AIOps platform, event and alert volumes can quickly spiral out of control. There is also the problem of identifying how alerts from different systems correlate, particularly if these systems and their teams are siloed. Crucially, there is no built-in intelligence to help identify and predict issues before they become critical, and therefore advanced tools and technologies, such as machine learning, cannot be harnessed to self-heal.  

To ensure an AIOps platform can be effectively architected, an advanced understanding of monitoring data must first be established. For DevOps engineers, who are aiming to implement AIOps capabilities, creating a monitoring platform that enables alerts to be prioritized and then fed into advanced remediation tools is a must. P1 (Priority 1) incidents, of course, will always require an immediate response, but incidents of a lesser priority can often be seen as a priority by customers. Similarly, a combination of lesser events across systems may lead to more significant incidents. To understand and respond to these demands, and to correlate alerts across systems, a robust monitoring system must be in place.  

The context of incidents is essential if remediation — and the rules that govern the automation of this process — is to be effective. It must also be remembered that more advanced monitoring will deliver more alerts, so the capability to scale monitoring will become essential. This is where cloud-native DevOps platforms deliver significant value, as they provide the means to quickly manage increased volumes of data.  

Shifting Left and Right 

To move towards an AIOps model, a combination of shift-left and shift-right practices and tools should be implemented. This means monitoring is prioritized early on in development while feedback from production is continuously incorporated. Once monitoring and alerts are managed at scale under this model, machine learning and other advanced analytics technologies can be harnessed through an AIOps platform to automate these processes, leading to more proactive, bespoke, and dynamic insights and remediation. Ultimately, this leads to organizations achieving greater resilience by ensuring service level objectives are met, delivery improves, and customer satisfaction increases.   

Without an AIOps platform, remediation would require subject-matter experts (SMEs) across different domains, from cloud infrastructure to application architecture, to meet in order to determine the root cause of an incident, which becomes a drain on time and resources. An AIOps platform can ensure automatic engagement of relevant SMEs by alerting them as soon as a P1 incident occurs, leading to less disruption and targeted remediation.  

Improving Developer and Customer Experience 

AIOps is essential if service providers wish to establish an advanced DevOps posture. It enables developers to feed into a secure CI/CD pipeline, thus pushing changes to production with confidence, as quality assurance is automated — furthering an organization’s shift-right capabilities. While this, of course, reduces the burden on developers to establish quality gates and reduces requirements around peer-reviews, the model also delivers increased customer satisfaction, as applications and features can be updated securely and frequently, while service availability is maintained and optimized.  

Research has shown that the majority of incidents (74%) are detected by customers before support teams are aware of a problem. When we consider that 66% of existing monitoring solutions only identify less than half of all performance issues or outages, and that growing IT complexity — typically led by accelerated cloud adoption — is leading to more outages, a move to more intelligent solutions is clearly required. Customers today not only expect service providers to maintain service level availability of 99.99% and 99.999%, they also require visibility of service performance.

A monitoring platform is able to provide this visibility through advanced reporting and data visualization tools, allowing multipurpose dashboards to be created with ease. This data can then be used by DevOps engineers to create self-healing runbooks that can be built into an AIOps platform — once again improving developer experience.  

The ultimate goal for DevOps engineers when architecting a DevOps platform is to create an environment that feels as though it was made for the developer, by the developer. Reducing the time developers spend implementing various capabilities, such as security, testing, and monitoring features, allows them to focus on improving the delivery of their services, creating the optimal experience for both the developer and the customer. Bringing automation into the remediation process through an AIOps platform furthers this enhancement as the chances for potential breaks in production are vastly reduced. This is the model that all service providers aim for with their DevOps strategy.