Application downtime is like a one-two punch. Not only does downtime cost you financially but there’s also an extensive opportunity cost associated with downtime. Downtime results in negative customer experiences, a loss of trust, financial ramifications and can decrease overall operational efficiency. Not only are you spending more time responding to incidents and fixing errors, but you’re spending less time building new features and services.
The extent of the real costs of downtime may surprise you. When application downtime hits you, there are consequences that build upon one another. So, you can see the importance of closely monitoring system health, quickly alerting on incidents, and remediating problems before they lead to application downtime. Robust systems are dependent on a streamlined process for incident response, leveraging both the power of people and automation.
That’s where machine learning comes in. Commonly referred to as MLOps in the IT operations and DevOps community, machine learning fits into the software delivery and incident management lifecycles to help people quickly understand system concerns, many times before they even happen.
So, let’s first discuss the pitfalls of experiencing application downtime and how teams can start leveraging MLOps to mitigate any potential downtime.
Application downtime causes headaches for your customers and your team. Because of the wide-ranging effects of downtime, it’s imperative to understand the pain it can cause and how machine learning can be used to ease these pains.
Clearly, application downtime affects customers. In today’s integrated digital world, customers expect the uptime of highly performant applications. The rise in SaaS-based business models and CI/CD means interconnected applications consistently rely on one another. This is why it’s so important to keep points of failure and failover options in mind.
Understanding how your system functions under stress helps you plan for maximum uptime and build more reliable services. Avoiding downtime mitigates negative customer experiences and helps build trust in your service. With more trust in your applications and infrastructure, people speak positively about your service and encourage further adoption of your products.
In a recent article from The Rand Group, they stated that 98% of organizations said a single hour of downtime costs over $100,000. And, 33% of those enterprises said that one hour of downtime costs somewhere between $1 - 5 million. The financial costs of downtime hit you both internally and externally. When you experience downtime, the team uses a lot of time and resources to resolve the incident and you lose out on potential revenue.
The personal and behavioral toll on your team from downtime is also an underrepresented cost. This piece from ZDNet cited a Washington Post study that showed 6.2 hours of productivity are lost every day due to service interruptions. This downtime results in fatigue, confusion, and reduced cognitive function. Reducing stress and context-switching internally helps developers and IT operations teams spend more time building, deploying and maintaining new features, rather than spending time resolving incidents and avoiding downtime.
Everything spirals into lost opportunity cost. The time spent remediating an incident takes away from time planning, building or deploying new features and services. Application downtime reduces the opportunity your team has to stay innovative and build the future faster. So, you’ll need to leverage everything in your toolbox – from machine learning to collaboration – to make sure you’re creating more opportunity for your team and business.
Machine learning in DevOps and IT incident management has wide-ranging potential. Teams can move from reactive incident response to a holistic system for proactive incident management and system observability. MLOps will allow the people in an organization to spend more time driving value and less time navigating incident workflows and manually resolving incidents.
According to a recent white paper from Moogsoft, “40% of all large enterprises will use machine-learning-based systems by 2022 to complement and eventually replace their current IT monitoring systems.” MLOps will allow IT and DevOps teams to use supervised and unsupervised machine learning to quickly identify anomalies, define the “normal” in your system, detect efficient response techniques and recognize redundant alerts.
Machine learning can be used to learn from both machine data and response data such as alert routing, escalations and chat. For instance, MLOps could potentially automatically escalate an incident to someone who’s talked about Kafka or resolved previous Kafka incidents in the past. Based on historical data, machine learning could tell on-call engineers whether an incident is likely to resolve itself over time or if they need to take action to remediate the incident. Every step of the incident lifecycle – from incident detection to post-incident review and preparation – can benefit in some capacity from machine learning and make on-call suck less.
Machine learning in DevOps and IT operations allows you to build more reliable services, limit downtime and even potentially predict issues before they happen. By combining the power of MLOps and collaboration in IT service management and DevOps, you can create deep system observability and mitigate application downtime – leading to positive customer experiences and a better overall product.
Effective incident management relies on a holistic, collaborative relationship between systems and people. Download the free Incident Management Buyer’s Guide to learn more about other helpful tools and functionality in incident management solutions.