This post is about how AIOps will change the way IT Operations personnel (IT Ops) work and the new skill sets they have to adopt in an AIOps world. For a definition of AIOps, refer to the blog post: “What is AIOps?”
Gartner explains that an AIOps platform (figure 1) uses machine learning and big data to aggregate observational data (from monitoring systems output, job logs, syslogs, etc.) and engagement data (from ticketing, incident, and event recording system data) to produce a virtuous circle of continuous insights yielding continuous improvements and fixes.
Automation is both an input and output of AIOps. The results or statuses of automated workloads and jobs can be used like operational data and engagement data for analytic purposes. Manual improvements can take the form of automating tasks, responses, remediations, etc. Machine learning that handles analytics at scale and adjusts algorithms accordingly is a form of automated improvement, e.g. Amazon and eBay online shopping, machine systems stock trading, or Netflix recommendations. In practice, a solid foundation of Automation and orchestration across systems, processes and workflows is the ideal starting point for AIOps and ensures a greater likelihood of success.
The implications of implementing AIOps are significant not only in terms of technology, but also in terms of process, culture and skills. AIOps will produce a big change in IT Ops’ role in both the Data Center and the business, leading IT organizations to ask this question:
What happens to the traditional IT Ops role when you turn IT Operations tasks over to an AIOps system that can respond to issues, manage applications and infrastructure, and adjust for cost and business value faster than the human beings that oversee it?
The answer is that just as Data Centers evolve using new technologies, IT Ops must also evolve by learning and using new skills to manage these new technologies.
Traditional IT Ops work focuses on producing and maintaining consistent, stable environments for service and application delivery. It also is concerned with meeting customer/user expectations and planning for growth and change. Traditional IT Ops tools try and provide useful information for the execution of these tasks. Generally these tools use human domain knowledge or analytic techniques or are modeled on them.
AIOps uses big data, algorithms, and machine learning to examine the profile of IT and business data, determine what “normal” looks like, find what factors are causal and correlative when things aren’t normal, and automatically recommend or implement a response. Machines execute these steps at incredibly fast rates on exponentially increasing amounts of data.
With AIOps, IT Ops job skills expand to include auditing AIOps results. IT Ops will need to understand how and why the AIOps platform is producing the outcomes it’s recommending or implementing. In an AIOps environment, IT Ops personnel need an enhanced skill set that helps them oversee the machine’s work, rather than just performing the work themselves. The AI skills gap is very real as pointed out in this Forbes article which reveals “the AI skills gap is the largest barrier to AI adoption, although data challenges, company culture, hardware and other company resources are also impediments.” So, how can IT Ops teams take steps to avoid the AIOps skills gap derailing their AIOps initiatives? Here are four skills IT Ops personnel will need as the world transitions into AIOps and application-centric infrastructures.
In machine learning, there is a concept of ‘supervised’ and ‘unsupervised’ learning. Supervised learning is where one trains a system using sample (historical) data. When the system outputs expected results, it is considered ‘trained’ and can be applied to new data. Unsupervised learning is where no training data is provided and the system must organize and analyze data with no outside guidance.
