AI Engineer Roadmap
Complete tracks sequentially to unlock advanced machine learning and system design sections.
Python Concurrency
Master memory benchmarking, GIL, async await, decorators, and context managers.
Section Resources
Mathematical Stats
Learn MLE, Bayes theorem, PDF integrations, hypothesis testing, and Metropolis-Hastings MCMC.
Classical ML
Understand loss functions, Normal Equation, SVM kernels, PCA projection, and Expectation Maximization.
Deep Learning Foundations
Derive backpropagation gradients, build Adam updates, CNNs, LSTMs, and scaled dot-product attention.
Large Language Models
Build BPE tokenizers, LoRA layers, DPO losses, speculative decoding, and Rotary Embeddings.
RAG Engineering
Write chunkers, hybrid searches, candidate rerankers, multi-query decomposers, and GraphRAG databases.
AI Agent Systems
Program ReAct loops, function call validators, stateful graph routers, supervisor networks, and code executors.
AI System Design
Analyze distributed training memory, speculative serving, KV cache optimization, and training workloads.