AIOps is relatively new term that was emerged in 2016. It was originally coined by Gartner as an acronym for Algorithmic IT Operations and is now mostly interpreted as Artificial Intelligence for IT Operations. The term combines several concepts such as artificial intelligence (AI), machine learning (ML) and Big Data to improve and optimize the operation of today's complex IT systems.
The biggest challenge for traditional IT operations that AIOps targets is the complexity of modern IT architecture, that is mostly the result of accelerated digital transformation. This is coupled with exponentially growing unused/uncorrelated data coming from both old and new IT stacks. Traditional tools live in silos and lack the intelligence and automation to address these key challenges.
AIOps-enabled systems use Big Data collected and unified from multiple components of the IT stack, and artificial intelligence methods are applied to this data that automatically identifies abnormal behavior from normal and detects problems automatically. This automation allows users to have a good visibility into their systems with significantly reduced effort and time spend on manual analysis. Such features are increasingly in demand as IT systems become more complex and it is almost impossible to manage this exponentially growing complexity with limited human resources.
However, besides all these benefits, the implementation of most AIOps platforms also comes with major challenges: luck of data science expertise, need for expensive and specialized infrastructure and issues with data quality.
In general, AIOps applications covers many areas of IT operations. Microservices, network, cloud environment, services and physical components, as well as security, as AIOps tools are also able to detect and recognize security-related event such as such as denial-of-service or ransomware attacks.
