Operations | Monitoring | ITSM | DevOps | Cloud

AIOps, or artificial intelligence for IT operations, uses AI and ML technologies alongside big data, data integration and automation to help make IT operations smarter and more predictive. AIOps has come around as a response to a pressing need for optimizing operations and minimizing risks to the IT infrastructure in the modern IT ecosystem.

It has become cliché to be doing market predictions, but it certainly enables Enterprises to get a pulse on the market, get informed, evaluate and strategize for course correction. My post-pandemic 2021 Predictions, highlighted the coming out party for AI/ML Ecosystem across multiple regulated verticals. My 2022 Predictions discussed the rise of the Data Economy and Data becoming the new source code.

This need for AIOps was simmering conveniently and gradually reaching its threshold when the pandemic suddenly hit the world, pushing organizations into remote work. The sudden, global-scale change raised challenges for IT operations teams to monitor and detect incidents in a distributed environment and maintain cybersecurity and compliance. While the pandemic pushed some organizations into the reality of remote work, others were already on their way to digital transformation.

DevOps was conceptualized to bridge the collaborative gap between developers and IT operations. Previously, developers worked independently of operations teams, shipping their work to the IT team and moving on. DevOps created a shared sense of ownership of a product, allowing development and ops teams to work in tandem for a more streamlined and efficient workflow.

As per the latest forecast by Gartner, worldwide IT spending is projected to amount to $4.6 trillion in 2023, up by 5.1% from 2022. The demand for IT will be strong in 2023 as enterprises launch digital business initiatives to respond to global economic challenges. In a downward economy, conventional wisdom warrants reducing costs.

NASA’s Nov’16 2022 launch kicked off highly anticipated Artemis 1 mission, sending an uncrewed Orion capsule on a nearly 26-day trek to the moon and back. The Space Launch Systems (SLS) megarocket appeared to perform exactly as planned during the liftoff, and was “simply eye-watering,” said the agency. Drawing analogies, we here at CloudFabrix had our own “Artemis 1 moment”.

According to one survey, 94% agree that AIOps is “important or very important” to manage network and cloud applications performance. AIOps intends to help customers contextualize humongous data volumes and streamline IT operations with automation. As IT infrastructure grows in complexity, alerts flood IT Ops centers and Ops teams drown in managing the deluge.

The absence of topology can be a key inhibitor for AIOps tools, creating blind spots for AIOps as they only have access to event data. A topology, an IT service model, or a dependency map is a real-time picture of tools and services that are connected and dependent on each other to deliver an IT service. Suppose an application is driven by cloud-native technology, connected with any kind of ephemeral systems (containers and microservices), and relies on storage, database, and a load balancing tool.

There’s plenty of loaded terminology and buzzword bingo when it comes to the latest advances in cloud application delivery. Especially when it comes to multi-cloud – which should merely mean multiple cloud instances when modern cloud applications really leverage multiple hybrid IT operating models, atop both existing business silos and newer microservices application workloads.