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AI Engineer
AI Deep Explorer | f...ย โขย 3m
If you're becoming an Al Engineer, here are 3 things NOT to focus on: (I wasted months on each of them) - Deep research on LLM architectures Advanced math - Chasing tools Back then, it felt like good advice... Now I know better. Let me go into more detail about each one (in no particular order): 1/ Deep research on LLM architectures You don't need to dive into the bleeding-edge stuff. Understanding the vanilla transformer architecture is enough to grasp the latest inference optimization techniques (required to fine-tune or deploy LLMs at scale). Just go through the "**Attention Is All You Need"** paper inside-out. Leave the complicated stuff to the researchers and fine-tuning guys. 2/ Too much math Yes, I don't think that studying advanced algebra, geometry or mathematical analysis will help you a lot. Just have fundamental knowledge on statistics (e.g., probabilities, histograms, and distributions). (this will solve 80% of your Al engineering problems) 3/ Focusing too much on tooling Principles > tools. Most of the time, you'll work with vendor solutions like AWS, GCP, or Databricks. Don't waste your energy chasing the newest framework every week. Stick with proven open-source tools like Docker, Grafana, Terraform, Metaflow, Airflow and build systems, not toolchains.
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