IMO, the classical mlops is closer to the genai stack than most people think. E.g. experiment tracking is the same: with classical mlops, you experiment with hy

IMO, the classical mlops is closer to the genai stack than most people think. E.g. experiment tracking is the same: with classical mlops, you experiment with hyperparams, with genai, you experiment with prompts. Similarly, finetuning is just an extension of training. Even vector databases for RAG is just vector search + databases, both of which have been around forever. The post-train world is what I find to be the most fun. Techniques like model merging, constrained sampling, and all the new creative techniques for inference optimization and faster decoding are super cool!