AI Engineer mindmap
Here is how I am seeing #AIengineer in my teaching. Welcome thoughts. Let me know if I missed anything
For context see my post on How to become an AI Engineer
Developing AI systems with the help of LLMs?
Prompt engineering
RAG
Fine tuning
AI evaluation metrics (RAG, AI as a Judge etc)
Langchanin
llamaindex
LLM assisted code development
OpenAI
Claude
Github copilot
OpenAI API
Machine learning and deep learning based systems that are not based on LLMs?
ML/DL algorithms in Python - from scratch
ML/DL algorithms Python - LLM generated
Model evaluation
Deployment
Data and Pipelines
MLOps
LLMOps
Autonomous AI agents
RAI and Ethics
Platforms
Cloud
Technologies
Graphrag and causal
Small language models
Software engineering
Open Source AI models
AGI
Reasoning
Multimodality
Gen AI |MACHINE LEARNING |DEEP LEARNING
2 个月great
CEO @ Render Networks | Advisor & Mentor helping Operators, Neutral Hosts and Builders de-risk rollout and get to market fast with industry leading AI and automation. Ex-IBM GM, -Nokia Bell Labs, -5 Airborne.
2 个月I think you could add some business logic. AI Engineers need to become more hybrid in terms of economic impact - if I was an AI engineer I’d put myself through at least a mini-MBA or similar to make sure I can apply my knowledge well and communicate well with my stakeholders.
Sales And Marketing Specialist at Amazon virtual assistant and freelancer
2 个月Great
Senior Solution Architect Generative AI
2 个月Great work , maybe missing some topics around AI tooling for engineering (vector database, no code / low code orchestrator, etc..). My idea is to have an idea of the tools available on the market to help AI engineers to create applications. Even if some of them may disappear in a few years, when the market becomes more mature, it's interesting to have this kind of global vision.
Project Manager in IT Consulting for Project Management Institute, Health and Telecommunications companies
2 个月Great job!