AIOps and LLMOps: The Evolution of DataOps and Beyond
This article was originally published on dataops.live.
This is the final piece in a four-part series on DataOps and AIOps, and it tackles a question I hear constantly: how do DataOps, AIOps, and LLMOps relate to each other?
The short version: AIOps is DataOps extended to the operational lifecycle of AI models — training, deployment, monitoring, and retraining. LLMOps is a specialized subset focused on the unique challenges of large language models, where fine-tuning processes mirror data transformation but produce improved models rather than transformed datasets.
What makes this interesting is how the boundaries are blurring. When DataOps started, it was primarily about SQL pipelines and data transformation. The introduction of Snowpark brought Java and Python development into Snowflake, which meant data teams suddenly needed software development practices alongside their data management practices. That convergence hasn't stopped — it's accelerating.
The practical implication is that modern data platforms require integrated approaches spanning DataOps, DevOps, and CloudOps. Multi-agent AI systems — where multiple models collaborate across different functions — demand expertise across all three domains simultaneously. That's a significant hiring and organizational challenge.
Snowflake's Cortex platform is an example of how this convergence plays out in practice, enabling continuous model fine-tuning and validation directly within the data platform. The organizations that will succeed are those that build integrated automation across these domains rather than treating each as a separate discipline.