Track B - AI Systems and Orchestration Engineer Progress Tracker

Track your progress through the universal foundation and Track B - AI Systems and Orchestration Engineer.

Level 1: Prerequisites (~15 hours)

• [ ] Python for AI - 3h
• [ ] Linear Algebra for AI - 3h
• [ ] Probability & Statistics for AI - 2h
• [ ] Neural Networks - 3h
• [ ] Deep Learning Fundamentals - 2h
• [ ] NLP Fundamentals - 2h

Level 2: GenAI Foundations (~20 hours)

• [ ] Transformers - 4h
• [ ] Attention Mechanism - 3h
• [ ] Tokenization - 2h
• [ ] Embeddings - 3h
• [ ] Modern LLM Architectures - 4h
• [ ] Scaling Laws & Pre-training - 4h

Level 3: Core GenAI Techniques (~25 hours)

• [ ] Large Language Models (LLMs) - 3h
• [ ] Prompt Engineering - 2h
• [ ] Context Engineering & Long Context - 3h
• [ ] Function Calling, Structured Output & Tool Use - 3h
• [ ] Retrieval-Augmented Generation (RAG) - 4h
• [ ] Fine-Tuning LLMs - 4h
• [ ] AI Agents - 4h
• [ ] LLM Evaluation & Benchmarks - 2h

Track B - AI Systems and Orchestration Engineer

• [ ] Graph RAG & Advanced Retrieval - 3h
• [ ] Multi-Agent Architectures - 3h
• [ ] Agent Evaluation & Observability - 3h
• [ ] Agent Memory Systems - 3h
• [ ] Agentic Protocols & Frameworks - 4h
• [ ] Advanced Fine-Tuning for LLM Adaptation - 4h
• [ ] Reinforcement Learning for LLM Alignment - 4h
• [ ] Knowledge Distillation & Model Compression - 3h
• [ ] Synthetic Data & Data Engineering for LLMs - 3h
• [ ] LLM Evaluation Deep Dive - 3h
• [ ] Hallucination Detection & Mitigation - 3h
• [ ] Vector Databases - 3h
• [ ] Model Serving for LLM Applications - 3h
• [ ] Monitoring & Observability for GenAI Systems - 3h
• [ ] Cost Optimization for GenAI Systems - 3h
• [ ] LLMOps & Production Deployment - 3h
• [ ] Embedding Fine-Tuning - 3h
• [ ] Long-Context Engineering - 3h
• [ ] Retrieval Evaluation - 3h
• [ ] Guardrails & Content Filtering - 3h
• [ ] LLM Routing & Model Selection - 3h
• [ ] Document Parsing & Extraction - 2h
• [ ] Data Flywheel Design - 2h
• [ ] AI UX Patterns - 2h
• [ ] Model Merging - 3h