The IT landscape changes at a breakneck pace, and complexity has become the name of the game.
Microservices replace monoliths and infrastructure diversifies in lockstep. Teams deploy Kubernetes layers, adopt wide-ranging cloud services and remotely manage complex workstation configurations. IT professionals wear multiple hats as they become inundated with expanded workflows and innumerable tasks.
Organizations increasingly turn to AIOps to address this complexity, but AIOps tools alone are insufficient. IT pros need certain skills to put the technologies and techniques into action and deliver the best results for the business.
What is AIOps?
The premise of AIOps is relatively simple: Infuse IT operations with artificial intelligence and other forms of automation. To put AIOps into practice, IT teams use third-party software systems and platforms to offload multiple tasks to applications and algorithms. The AIOps processes redefine everyday challenges, including:
- performance monitoring;
- data analysis;
- services management;
- general automation;
- issue remediation, event logging and outages; and
The field is ultimately focused on efficiency and consolidation through automation.
AIOps skills to get ahead
While AIOps hasn't caught fire yet, expectations are high. By 2023, 40% of DevOps teams will augment their IT monitoring tools with AIOps capabilities, according to Gartner. Businesses will need to hire staff to support these initiatives, so job seekers will have a massive advantage if they're already well versed in AIOps.
However, those seeking new employment will need numerous specialized skills, as AIOps responsibilities cross disciplines. But whether you want a job in IT, DevOps or SecOps, there are several core AIOps skills that professionals can apply across those positions.
1. Monitoring centralization and interpretation
Machine learning and AI work best with unified data sets. AIOps professionals must harmoniously integrate data ingestion -- monitoring -- tools with data analysis tools, which often means connecting open source tools to closed source tools.
These duties aren't limited to one technological element. For example, an AIOps professional might coordinate data collection from applications, general infrastructure and networking hardware or software. This data forms a pool. From there, they must choose the correct machine learning tools for proper interpretation.
And while AIOps veterans don't uncover patterns manually, they do interpret them. Automated monitoring is extremely valuable, but it won't deliver business value fully without a keen eye. Interpretation skills are essential to roles in IT, DevOps and even security.
2. Systems design and advocacy
AIOps is predicated on solving puzzles, which requires proper tooling. Professionals dive deep into their infrastructure to learn its strengths, weaknesses and overall composition. They must then design AIOps systems based on systemic priorities and/or pain points.
Finding the right design means becoming a researcher. Sound systems design hinges on whether your IT organization adopts external tools or crafts internal alternatives. These detailed cost-benefit judgements separate AIOps professionals from their colleagues.
Furthermore, business analytics are caught with bespoke designs -- not one-size-fits-all configurations. AIOps systems designers need to sell teams and management on what's best, regardless of any existing organizational frugality or tooling biases.
3. Data analysis and management
Exceptional data scientists are costly and hard to find. When an organization can't afford to hire enough, AIOps professionals are often left to handle many of the tasks that would otherwise fall to those individuals.
AIOps pros use machine learning tools to close the knowledge gap via automation and visualization. They might need to be versed in extract, transform, load (ETL) procedures so they can address data normalization during ingestion.
Alternatively, they might need to brush up their tagging skills. Under the premise that each piece of incoming data has unique characteristics, an AIOps pro would use algorithmic tools to learn how data is categorized and referenced. Data becomes an object, and the professional must understand an object's relationship to others.
AIOps pros should understand the pros and cons of different approaches and determine which works best for their business' needs. They should know the difference between supervised and unsupervised algorithms and concern themselves with assessing data quality, accuracy and relevancy as it's gathered.
4. Security and event recognition
Proper configuration is key to realizing the fullest potential of AIOps tools. For example, setting threshold or event-based alerts is crucial to infrastructure oversight. And while these platforms are adept at catching nefarious actors or notable problems, it's up to the IT team to respond.
AIOps technologies break down barriers within an organization, so you'll need to develop a keen eye for security and acquaint yourself with multiple systems. For example, AIOps threat detection functions can span multiple business facets. Those involved in AIOps can alert other departments to key issues and offer remediation recommendations.
5. Cross-functional teamwork
As was just mentioned, AIOps touches many facets of an organization and brings together people from multiple departments. AIOps professionals must understand micro- and macro-level priorities, work with different personalities and balance actions with goals.
Despite all the focus on background automation, success ultimately hinges on sound communication and outreach. Drive the business forward with lateral communication across teams and vertical outreach to leaders. AIOps professionals don't just help companies innovate, they demonstrate how technologies and techniques can lead to long-term prosperity.
To land this type of role, it helps to be gregarious and to speak the language of the technical crowd and the C-suite. AIOps insights can aid marketing teams as readily as DevOps teams.