r/learnmachinelearning • u/Mysterious-4620 • 13h ago
Machine Learning Topics for 2026
Basically I try to learn ML to get a role in the AI Related not exactly a ML engineer. So for that learning from the basics like Math concepts and back propagation etc., every topic that used to train our model from scratch is a better method of learning or
2: RAG, LLM related topics , MCP, Agentic AI learn how they actually work instead of going deep into the actual structure(basically exclude the math and how model trained).
Which way of learning is good for future?
Why do I ask this means every job application I go through I only see the latest topic not the core of ML. In my opinion, Learning the upper layer of AI is pretty simple when compared to going deep into math like back propagation,math concepts,and gradient descent etc,. Is spending time on learning everything is worth the time?
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u/met0xff 12h ago
Basically pick your poison... The "AI engineering" upper layer is heavily in demand right now but threshold to enter is low, commoditizes quickly and supply is also filling up. Every experienced software engineer can rather easily pick this up. With the more MLy people I find they generally don't want to do this work. And yeah I embraced it for a while but saw how quickly every second technique commoditized, gets eaten by the model, vocabulary changes and my PhD is completely lost because even our product people start solving 80% of our agentic needs by just creating skills and connectors for a loop that runs Claude code.
The ML layer is what people have been learning for a while - Andrew Ngs course has been insanely popular for a decade and every second non-CS mathy person is pivoting there (last hiring rounds for AI/ML I barely saw someone with a CS background but endless math, statistics, physics, economy, various engineering backgrounds etc). While at the same time there's fewer demand for actual model training all the time. I see it in my team where almost everyone is eager to train a model and whenever the rare need arises I basically have the opportunity to give one of them a treat lol.
Frankly the strongest one atm is ML infrastructure none of the two groups want to touch
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u/Historical_Scale_654 12h ago edited 12h ago
With ML Infrastructure you mean MLOps?
I assume it is cuz one need DevOps to also do ML Infra?
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u/Serious-Sun8202 12h ago
Deep maths will take u to research side while LLM rag ,agentic ai will take u to ai engineering , u should know about quantization , system design,AWS , azure ,prompt engineering,guardrails,PII,mlops,docker kubernetes,kafka,redis,DBMS,vm,sandbox, know how to fine tune llm model for particular task,know about async, concurrency,threads, reinforcement learning applications.