This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. With orchestration and monitoring playing such key roles in DevOps, the emerging trend of using artificial intelligence (AI) to support and even automate operations roles by delivering real-time insights about what's happening in your infrastructure seems an obvious fit. DevOps is about improving agility and flexibility; AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. That's especially true as trends like microservices, hybrid cloud, edge computing and IoT increase the complexity of app infrastructures -- and the number of logs that you might have to look at to find the root cause of an issue, and the number of people who need to be in a conference call or chat room tracking down what's gone wrong and how to fix it.
It's hard to visit a tech website these days without reading an article about how artificial intelligence (AI) is poised to uproot entire industries and workflows. As it turns out, IT systems operations fall under the umbrella of things that are likely to change or already have due to AI. That shift created a new IT category called algorithmic IT operations -- or AIOps for short. The basic definition of AIOps is that it involves using artificial intelligence and machine learning to support all primary IT operations. The goal is to turn the data generated by IT systems platforms into meaningful insights.
If your data pipelines are growing in complexity and beyond the point where you can manage them, you're not alone. Today, they have become so massive and are crisscrossed by so many dependencies that it can be hard to see how all the components fit together, and hard to identify issues and opportunities that impact app performance and availability. Data stacks combine many disparate elements for data gathering and analysis, among other functions -- and exponential data growth in most organizations only adds to the challenge. In such an environment, simply monitoring performance and taking reactive measures when performance lags is no longer a viable approach. Today, with AIOps (Artificial Intelligence for IT Operations), a correlated data model helps you discover the full context of your apps and system resources so that you can adequately plan, manage, and improve performance.
With increasing efficiency and sophistication, the IT environment is becoming extremely complex too. The recent shift to microservices and containers has further added to the already large number of components that go into a single application, which means the challenge is equally big when it comes to orchestrating all of them. The ability of IT Ops teams to handle such complexities is fairly limited and hiring more resources to configure, deploy, and manage them is not very cost-effective. This is where artificial intelligence for IT Operations (AIOps) comes into play. None come close to AIOps when it comes to leveraging Big Data, data analytics, and machine learning to offer a high level of customization along with invaluable insights necessary to cater to modern infrastructure.
AIOps platform market size is growing exponentially. According to MarketsandMarkets latest survey, the AIOps market size will reach USD 11.02 billion by 2023 and USD 237 billion by 2025. But, what is AIOps, what it is designed to do, how it is developing, and why AIOps is the next big thing in IT operations? If you are also looking for answers for such questions, then your search ends here. AIOps is the application of Artificial Intelligence (AI) to IT operations.