Get up to 50% off! Limited time only: Learn More.

MLOps Potential in the Incident Lifecycle

Dan Holloran December 26, 2018

DevOps Monitoring & Alerting
MLOps Potential in the Incident Lifecycle Blog Banner Image

Machine learning is being applied to numerous functions across nearly every industry. Today, in software development and IT operations, machine learning is helping teams both build applications faster and fix problems faster. This use of machine learning across DevOps teams has resulted in the idea of MLOps. MLOps has great potential across software delivery workflows, IT operations, and the overall incident lifecycle.

In this post, we’re diving into the potential MLOps has for helping DevOps and IT teams improve incident response and remediation workflows – leading to lower MTTA/MTTR and a shortened incident lifecycle. With the sheer amount of human and machine data we’re now able to collect, combined with the principles of supervised and unsupervised machine learning, we can now turn this data into insights that improve the incident management process.

First, we need to understand the normal incident lifecycle and how people are involved throughout the process. Then, we can dive into the ways machine learning operations can be applied to make this process easier for the people on your team. We’re just now starting to realize some of the possibilities for MLOps, so it’s exciting to see the potential and think about the future of MLOps in the incident lifecycle.

MLOps Across the Incident Lifecycle

The five main steps of the incident lifecycle cover the end-to-end workflows for understanding an incident, remediating the issue, and proactively preparing yourself in order to build resilient services. Let’s see how humans and machines could interact in each of the five incident lifecycle steps to create a culture of DevOps and MLOps collaboration.

Creating a Culture of Reliability

  • Step 1: Detection

Of course, you need to first detect an incident in order to start fixing it. A detailed and organized system for monitoring service and infrastructure health helps people understand how the machines are responding to a problem or incident. Then, a centralized system for alerting and collaboration allows people to easily view the monitoring data, receive alerts when necessary, and determine what’s wrong.

A holistic system of monitoring, alerting, and communication leads to highly observable systems and helps people detect machine issues quickly. MLOps could help your monitoring and alerting tools learn from past incidents in order to identify more leading metrics or indicators that may show an incident is likely to occur. This way, DevOps and IT teams can be more proactive about identifying an issue and taking steps to quickly respond to system health risks.

  • Step 2: Response

With a strong system for detecting an incident, the team needs to know how they respond. Every DevOps and IT operations team needs to know exactly what’s expected of them when they’re on-call and need to respond to an incident. How are alerts re-routed or escalated? What are the time expectations for acknowledging and resolving an incident? What’s the best method for teammates to communicate in real-time during a firefight?

MLOps will allow you to learn from system behavior in order to optimize human response when an incident happens. A centralized system for monitoring, alerting, and collaboration makes incident response easier, but it will also allow for a more detailed approach if your team ever decides to leverage MLOps in the incident lifecycle.

  • Step 3: Remediation

Typically, the actual resolution of an incident takes up a very small portion of the entire incident lifecycle. But traditionally, humans spent a large amount of time detecting and responding to the incident in order to find an easy resolution. Or maybe, the incident self-resolved after a certain amount of time.

Remediation could potentially be much simpler with MLOps. For instance, machine learning could allow the system to correlate similar incidents, automatically populate runbooks or other annotations, or even let an on-call responder know that an incident is likely to self-resolve. MLOps should be centered around helping the people behind the system better understand an incident and allow them to rapidly move through the incident lifecycle.

  • Step 4: Analysis

After incident remediation, many teams simply walk away. The incident has been resolved, there’s nothing else to do, right? Wrong. Steps four and five of the incident lifecycle may actually be the most important ones for creating resilient teams and building more robust architecture. If you only work through the first three steps of the incident lifecycle, you’re not really learning from your mistakes.

In the analysis phase, your team will conduct thorough post-incident reviews to understand what went well and what didn’t go well. From here, you can refine incident workflows, make system adjustments and tweak tooling in order to help maintain more consistency in your application and infrastructure. MLOps could help identify incident trends over time or expose weak points in your architecture. With every incident that occurs, the machine learning algorithm learns more and can be refined to make your team even more efficient.

  • Step 5: Readiness

With MLOps involved in the first four steps of the incident lifecycle, you’ve learned more about your system than if you solely analyzed your systems and processes with people. Now, your team is more ready than ever to respond to the next incident. Working machine learning capabilities into a holistic incident management process shortens the incident lifecycle and reduces MTTA/MTTR over time. With each new incident, your team learns more and can find optimal ways for surfacing the tools and information required to efficiently manage any firefight.

The Importance of Human Response

Contrary to popular belief, MLOps doesn’t replace human involvement in the incident lifecycle. The purpose of MLOps is to make every step of the incident lifecycle easier for the on-call incident response teams. No matter the structure of your team – DevOps, siloed IT operations, SRE, or a blend of all three – a tighter relationship between systems and people always drives operational efficiency, system uptime, and business value. As the potential of MLOps develops and takes shape in the coming years, remember that MLOps should always make your people better.

Efficient incident management is all about combining the power of technology with human collaboration. In our free Incident Management Buyer’s Guide, learn about everything an incident management solution needs to help DevOps and IT teams shorten the incident lifecycle.

Ready to get started?

Let us help you make on-call suck less.