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As IT systems continue to evolve and grow, their scale and complexity are becoming increasingly difficult to manage. The sheer volume of data these systems generate is overwhelming, and -- without sufficiently intelligent monitoring and analysis tools -- can result in missed alerts, opportunities and excessive (and expensive) downtime.
With the advent of big data and machine learning, however, a new category of IT operations tool has emerged: AIOps.
What is AIOps?
AIOps stands for artificial intelligence for IT operations and it is the practical application of artificial intelligence (AI) to enhance, support and automate IT operations. AIOps uses analytics and machine learning (ML) to monitor and analyze complex streaming data in real time, helping teams detect and react to potential issues more quickly.
Why AIOps is important
As more businesses undergo digital transformation, AIOps is increasingly important due to:
Increased security threats and compliance requirements
Technology and systems increasing in scale and complexity
The continually growing quantity of data these systems generate
More complex and varied information and analyses required by different stakeholders
Too little available human talent to deal with all of the above
An AI operations platform helps by enhancing and automating a wide array of IT tasks and processes. Rather than relying on people to manually cope with the growing flood of data generated by modern IT systems, AIOps takes care of the monitoring and analysis parts, leaving your DevSecOps and Site Reliability Engineering (SRE) teams free to focus on other things.
What can AIOps do?
Through real time processing of streaming data, an AIOps platform can help provide continuous insights across IT operations, including, among other things:
Malware traffic detection
Root cause analysis
In addition to providing these sorts of insights, AIOps platforms can also be trained to respond to alerts automatically so many issues can eventually be resolved without human intervention.
How does AIOps work?
AIOps works by turning data into insights through analysis and machine learning.
Modern IT systems generate an enormous (and ever increasing) quantity of data across their many and varied components. And this data is often noisy, redundant, inconsistent and coming in at speeds impossible for a human to comprehend.
This is where AIOps comes in.
An AIOps platform:
Ingests both historical data and real-time streaming data from across the IT environment.
Filters out the noise so only the most relevant data is analyzed.
Discovers and understands patterns within that data.
Issues alerts or events when potential problems are detected
Identifies root causes for problems and proposes (and potentially implements) solutions.
And this is not a simple “one and done” process -- through ongoing machine learning, AI operations platforms continue to improve, becoming more efficient and effective over time.
6 AIOps benefits
While this is not a comprehensive list of all the benefits AIOps tools can provide, here are six ways it can help IT operations teams and organizations as a whole.
1. Reduce downtime
Application and system downtime can be costly in terms of lost revenue, lower productivity and damage to your organization’s reputation. AIOps helps DevSecOps and SRE teams detect and react to emerging issues before they turn into expensive and damaging failures.
2. Improve operational confidence
AIOps can take the guesswork out of many IT operations processes and tasks by helping pinpoint potential issues, evaluating their impact on your environment and providing step-by-step remediation guidance.
3. Continually manage vulnerability risks
As environments grow in size and complexity, there are an increasing number of risks to manage. Manual methods are unable to keep up with the rate of change, but AIOps tools help you identify, analyze, prioritize and remediate vulnerability risks.
4. Optimize skills and resources
By providing root cause analysis and remediation guidance, AI operations can help your team solve problems more efficiently while simultaneously deepening their own understanding and skills.
5. Focus on innovation
With much of the day-to-day drudge work required to “keep the lights on” eliminated, AIOps gives teams the freedom to develop and deliver more strategic and higher value projects and innovations.
6. Control complexity
AIOps can help teams understand the differences between systems, streamlining system patch and configuration management, simplifying operations and improving reliability.
On the flip side, however, implementing an AIOps platform also presents a number of challenges.
Expertise: there’s an intimidating barrier to entry because extensive data science expertise is required
Infrastructure: expensive and specialized infrastructure and deployments are needed
Time to value: AIOps systems can be difficult to design, implement, deploy and manage, so it can take some time to see any return on investment
Data: the volume, quality and consistency of data produced by modern IT operations can be overwhelming and difficult to wrangle into something that can be used for modelling
If you’re unfamiliar with AI and ML, spend some time learning about the concepts and existing capabilities so you’re familiar with what is (and isn’t) currently possible.
And, if you haven’t already, a practical thing you can do right now is start to build your organization's DevSecOps pipeline and culture.
A robust DevSecOps system improves operational efficiency and security, and allows you to take advantage of existing automation tools and platforms. By developing these capabilities now, it will be easier for your organization to adopt new and improved AI and ML tools as they continue to evolve.
About the author
Deb Richardson is a Contributing Editor for the Red Hat Blog, writing and helping shape posts about Red Hat products, technologies, events and the like. Richardson has over 20 years' experience as an open source contributor, including a decade-long stint at Mozilla, where she launched and nurtured the initial Mozilla Developer Network (MDN) project, among other things.