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great list! as someone who's also trying to map the ai engineering landscape... i wonder what u think of adding other parts of the AI stack (https://www.latent.space/p/dec-2023). right now you have 4 categories and those are all in the text/code-heavy RAG/Agent world, but i think the space has broadened out a bit as i see it. for example, you could add:

- finetuning/other post-pretrain model tools (axolotl, mergekit <- all made and used by people without traditional ML engineer/researcher background)

- multimodal models/frameworks like vocode and comfyui

- AI UX tools like vercel ai sdk

- synthetic data generation tooling? whatever the nous pple have made

open question whether inference frameworks like llama.cpp/ollama or vllm and tgi count as AI Eng tools? again given the background of ggeranov and the students behind the other projects, arguably yes but ofc it starts to bleed into classical mlops here. (update: i see u have them in the "model development" category, ok fair)



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!




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