@_avichawla: DevOps vs. MLOps vs. LLMOps, clearly explained: Many teams are trying to apply DevOps practices to LLM apps. But DevOps…

DevOps vs. MLOps vs. LLMOps, clearly explained: Many teams are trying to apply DevOps practices to LLM apps. But DevOps, MLOps, and LLMOps solve fundamentally different problems. DevOps is software-centric. You write code, test it, and deploy it. The feedback loop is straightforward, i.e., does the code work or not? MLOps is model-centric. Here, you're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes. LLMOps is foundation-model-centric. Here, you're typically not training models from scratch. Instead, you're selecting foundation models and then optimizing through three common paths: - Prompt engineering - Context/RAG setup - Fine-tuning But here's what really separates LLMOps: The monitoring is completely different. In MLOps, you track data drift, model decay, and accuracy. In LLMOps, you're watching for: - Hallucination detection - Bias and toxicity - Token usage and cost - Human feedback loops This is because you can't just check if the output is "correct." You need to ensure it's safe, grounded, and cost-effective. The evaluation loop in LLMOps also feeds back into all three optimization paths simultaneously. Failed evals might mean you need better prompts, richer context, OR fine-tuning. So it's not a linear pipeline anymore. One more thing: prompt versioning and RAG pipelines are now first-class citizens in LLMOps, just like data versioning became essential in MLOps. And the ops layer you choose should match the system you're building. If you want to go deeper into LLMOps, I wrote a full LLM engineering roadmap a while back. It walks through the eight pillars of building LLM systems, starting at prompt engineering and ending at observability and safety, with free and open-source resources attached to each one. You can read it below.