- AIops uses AI to detect anomalies and automate IT operations.
- MLops manages the full lifecycle of machine learning models.
- Finops governs cloud costs and financial accountability.
- Dataops applies devops-style principles to data pipelines.
Some go further still. Rana points to “devsecprivacyops” and even “devsecprivacyAIops,” which combine privacy-by-design, regulatory compliance, and AI-powered operations. These may sound convoluted, but they illustrate the same basic tendency: taking devops principles and applying them to specialized contexts.
An ops reality check
At some point, the alphabet soup stops being helpful. IronPDF’s Rimington says that the overlap is so significant that most new “ops” terms are just marketing. “Cloudops and AIops are just extensions of devops, but applied to different kinds of infrastructure,” he says. “We concluded that a base knowledge of devops fundamentals, such as automation, monitoring, and collaboration represents 80% of what you need for any of the other shapes.”
Voltage’s Krizek makes a similar point. “While it is not necessary for every IT professional to master each one individually, understanding the principles behind them is essential for navigating modern infrastructure,” he says. “The focus should remain on creating reliable systems and delivering value, not simply keeping up with new terminology.”
