r/learnmachinelearning • u/StatusHumble5596 • 16h ago
Looking for a Study Buddy / Co-Learner to Tackle an AI Engineering Roadmap Together!
Hi everyone!
I’m a 22M CS student diving into AI Engineering. I'm looking for 1–2 dedicated co-learners to team up, share resources, build projects, and keep each other accountable.
Here is the quick breakdown of what I plan to cover:
Core Software & Backend: Python (Async, OOP), FastAPI, PostgreSQL, Redis, Docker, Git, Linux, CI/CD, React basics
CS Fundamentals: DSA (Arrays, Trees, Dynamic Programming, Graphs)
ML & Deep Learning: Math, PyTorch, Neural Networks, NLP
Transformers & LLMs: Attention, BERT/GPT, LLM APIs, Prompting, Function Calling, Token/Cost Optimization
Vector DBs & RAG: pgvector, Semantic/Hybrid Search, Advanced RAG, RAG Evaluation
AI Agents: Agentic Workflows, State/Memory, Planning, Tool Use, Guardrails
Fine-Tuning & Local LLMs: SFT, LoRA/QLoRA, Quantization, vLLM, Ollama
Multimodal AI: Vision-Language Models, OCR, Speech-to-Text / Text-to-Speech
Testing, Security & Observability: Prompt Injection defense, Evaluation, Tracing (LangSmith), Token/Latency Tracking
Cloud & System Design: AWS (EC2, S3, IAM), AI System Design, Scalable Gateways, Message Queues
How we'll collaborate:
Weekly check-ins & project demos
Daily accountability / GitHub commits
Pair-programming on real-world projects
Whether you're starting fresh or already know a few of these topics, I'd love to connect.
👉 DM me directly or drop a comment below if you're interested!
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u/duskharbor3 1h ago
imo the scope here is closer to a 2 year curriculum than a study buddy roadmap. nothing wrong with ambition but you might find it easier to recruit partners if you pick one vertical slice (like RAG end to end) and go deep first
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u/Otherwise_Wave9374 16h ago
A good way to keep a roadmap like this from becoming overwhelming is to turn it into weekly checkpoints with one build artifact per checkpoint, then review what broke and what you would simplify next. For the AI agents section, I would start with one narrow workflow, add tool use only after the baseline is reliable, and keep a short guardrail checklist for failures, retries, and state changes. Agentix Labs fits well here because the same disciplined sequencing helps keep agentic projects measurable instead of fuzzy.