AI/ML Engineer Learning Path
A structured 12-week journey through the Knowledge Vault for engineers building AI/ML-powered products. This path covers ML fundamentals (30 pages), deep learning (25 pages), LangChain/LangGraph mega guides, fine-tuning, guardrails, AI testing, model serving, GPU infrastructure, RAG architecture, AI agents, and production MLOps.
Who This Is For
- Software engineers transitioning into AI/ML engineering roles
- Backend engineers adding AI capabilities to existing products
- Data scientists who want to learn the engineering side of ML
- Anyone building LLM-powered applications in production
Prerequisites
- Solid programming skills (Python + one backend language)
- Basic understanding of APIs and databases
- Basic math (linear algebra, calculus, probability) -- or willingness to learn
- No prior ML experience required
Total estimated time: ~60 hours across 12 weeks
Learning Progression
Week 1-2: ML Foundations (Part 1 of 30 pages)
Estimated reading time: 6 hours
Build the math and conceptual foundations before touching models.
- [ ] Required -- Machine Learning Overview (15 min)
- [ ] Required -- Math Foundations (35 min)
- [ ] Required -- ML Workflow (25 min)
- [ ] Required -- Python ML Ecosystem (25 min)
- [ ] Required -- Data Preparation (25 min)
- [ ] Required -- Linear Regression (30 min)
- [ ] Required -- Logistic Regression (25 min)
- [ ] Required -- Evaluation Metrics (25 min)
- [ ] Required -- Cross-Validation (20 min)
- [ ] Required -- Model Selection (25 min)
- [ ] Reference -- Scikit-learn Cheat Sheet (10 min)
- [ ] Reference -- Python Cheat Sheet (10 min)
Checkpoint
After this section you should be able to: explain the ML workflow, implement linear and logistic regression, evaluate models with precision/recall/F1/AUC, and perform cross-validation correctly.
Week 2-3: ML Algorithms (Part 2 of 30 pages)
Estimated reading time: 6 hours
Master the classical ML algorithms that form the foundation of modern AI.
- [ ] Required -- Decision Trees (25 min)
- [ ] Required -- Random Forests (25 min)
- [ ] Required -- Gradient Boosting (30 min)
- [ ] Required -- Ensemble Methods (25 min)
- [ ] Required -- SVM (25 min)
- [ ] Required -- KNN (20 min)
- [ ] Required -- Naive Bayes (20 min)
- [ ] Required -- Clustering (25 min)
- [ ] Required -- Hyperparameter Tuning (25 min)
- [ ] Required -- Algorithm Selection Guide (20 min)
- [ ] Optional -- Feature Engineering Advanced (25 min)
- [ ] Optional -- Dimensionality Reduction (25 min)
- [ ] Optional -- Anomaly Detection (20 min)
- [ ] Optional -- Recommendation Systems (25 min)
- [ ] Optional -- Time Series ML (25 min)
- [ ] Optional -- ML Interpretability (25 min)
- [ ] Optional -- ML Checklist (15 min)
- [ ] Optional -- Topic Modeling (20 min)
- [ ] Optional -- Association Rules (15 min)
Checkpoint
After this section you should be able to: choose the right algorithm for a given problem, tune hyperparameters with grid/random/Bayesian search, and explain the bias-variance tradeoff.
Week 3-4: Deep Learning Foundations
Estimated reading time: 5 hours
Transition from classical ML to deep learning. Understand neural networks, PyTorch, and training techniques.
- [ ] Required -- Deep Learning Overview (15 min)
- [ ] Required -- Neural Network Basics (35 min)
- [ ] Required -- PyTorch Fundamentals (30 min)
- [ ] Required -- Training Techniques (25 min)
- [ ] Required -- Architecture Selection Guide (25 min)
- [ ] Required -- Transfer Learning (25 min)
- [ ] Required -- DL Checklist (20 min)
Checkpoint
After this section you should be able to: implement neural networks in PyTorch, apply training techniques (BatchNorm, dropout, LR scheduling), and choose the right architecture for a given task.
Week 4-5: DL Architectures (Part 2 of 25 pages)
Estimated reading time: 6 hours
Master the architectures that power modern AI: CNNs, RNNs, Transformers, and generative models.
- [ ] Required -- Transformers (30 min)
- [ ] Required -- Language Models (30 min)
- [ ] Required -- BERT Family (25 min)
- [ ] Required -- NLP Fundamentals (25 min)
- [ ] Required -- Text Generation (25 min)
- [ ] Optional -- CNN (25 min)
- [ ] Optional -- RNN & LSTM (25 min)
- [ ] Optional -- Diffusion Models (25 min)
- [ ] Optional -- GANs (25 min)
- [ ] Optional -- Multimodal Models (25 min)
- [ ] Optional -- Model Optimization (25 min)
- [ ] Optional -- Reinforcement Learning (25 min)
- [ ] Optional -- Papers Reading List (20 min)
Checkpoint
After this section you should be able to: explain transformer attention mechanism, understand the difference between BERT and GPT architectures, and fine-tune pretrained models.
Week 5-6: LLM Integration
Estimated reading time: 5 hours
Integrate LLMs into production with proper engineering around prompts, rate limits, costs, and fallbacks.
- [ ] Required -- AI/ML Engineering Overview (15 min)
- [ ] Required -- LLM Integration (35 min)
- [ ] Required -- OpenAI API (25 min)
- [ ] Required -- Anthropic Claude API (25 min)
- [ ] Required -- Prompt Engineering Advanced (30 min)
- [ ] Required -- Prompt Caching (20 min)
- [ ] Required -- Multimodal AI (25 min)
- [ ] Optional -- Vercel AI SDK (20 min)
- [ ] Optional -- HuggingFace (25 min)
- [ ] Reference -- LLM APIs Cheat Sheet (10 min)
Checkpoint
After this section you should be able to: build production LLM integrations with caching and fallbacks, implement advanced prompt engineering, and manage token budgets and costs.
Week 6-7: LangChain & LangGraph
Estimated reading time: 5 hours
LangChain and LangGraph are the dominant frameworks for building LLM-powered applications.
- [ ] Required -- LangChain (40 min)
- [ ] Required -- LangGraph (40 min)
- [ ] Required -- LangSmith (25 min)
- [ ] Required -- LlamaIndex (30 min)
- [ ] Required -- CrewAI & AutoGen (25 min)
Comparisons:
- [ ] Required -- LangChain vs LlamaIndex (20 min)
- [ ] Required -- OpenAI vs Anthropic vs Google (20 min)
Checkpoint
After this section you should be able to: build complex LLM applications with LangChain, implement stateful multi-step agents with LangGraph, trace and debug with LangSmith, and choose between frameworks.
Week 7-8: RAG & Embeddings
Estimated reading time: 5 hours
RAG is the dominant pattern for building AI products that answer questions from private data.
- [ ] Required -- RAG Architecture (40 min)
- [ ] Required -- Embeddings (35 min)
- [ ] Required -- Vector Databases (35 min)
- [ ] Required -- Data Annotation (25 min)
- [ ] Required -- Search Service Blueprint (40 min)
- [ ] Optional -- Elasticsearch Internals (25 min)
Checkpoint
After this section you should be able to: design a complete RAG pipeline, implement hybrid search (vector + keyword), choose chunking strategies, and evaluate retrieval quality.
Week 8-9: AI Agents
Estimated reading time: 4 hours
AI agents use LLMs to plan and execute multi-step tasks with tools.
- [ ] Required -- AI Agents (40 min)
- [ ] Required -- LLM Integration (35 min -- focus on function calling)
- [ ] Required -- AI in Production (30 min)
- [ ] Optional -- Job Queue Blueprint (40 min)
- [ ] Optional -- Circuit Breaker (20 min)
Checkpoint
After this section you should be able to: build ReAct and Plan-Execute agents, implement guardrails and human-in-the-loop, and debug agent reasoning traces.
Week 9-10: Fine-Tuning & Guardrails
Estimated reading time: 5 hours
Customize models for your domain and keep them safe in production.
- [ ] Required -- Fine-Tuning (35 min)
- [ ] Required -- AI Guardrails (30 min)
- [ ] Required -- AI Testing (30 min)
- [ ] Required -- ML Pipelines (30 min)
- [ ] Optional -- Model Optimization (25 min)
- [ ] Optional -- Text Generation (25 min -- focus on RLHF/DPO)
Checkpoint
After this section you should be able to: fine-tune models with LoRA/QLoRA, implement content safety guardrails, design AI evaluation suites, and build reproducible ML pipelines.
Week 10-11: Model Serving & GPU Infrastructure
Estimated reading time: 5 hours
Deploy and serve models at scale with proper GPU management and infrastructure.
- [ ] Required -- Model Serving (30 min)
- [ ] Required -- GPU Kubernetes (30 min)
- [ ] Required -- AI Infrastructure Overview (15 min)
- [ ] Required -- Docker Overview (15 min)
- [ ] Required -- Production Dockerfiles (25 min)
- [ ] Required -- Kubernetes Overview (15 min)
- [ ] Required -- HPA, VPA & KEDA (25 min)
- [ ] Optional -- AWS Lambda (25 min)
- [ ] Optional -- GCP Cloud Run (25 min)
- [ ] Optional -- Serverless Patterns (25 min)
Checkpoint
After this section you should be able to: deploy model serving endpoints with auto-scaling, manage GPU resources on Kubernetes, containerize ML models, and choose between serverless and container-based inference.
Week 11: AI Testing & MLOps
Estimated reading time: 4 hours
- [ ] Required -- AI Testing (30 min -- deep read)
- [ ] Required -- Test Architecture (25 min)
- [ ] Required -- Integration Testing (25 min)
- [ ] Required -- CI/CD Overview (15 min)
- [ ] Required -- GitHub Actions Deep Dive (30 min)
- [ ] Optional -- Property-Based Testing (25 min)
Week 12: Production Blueprints & Capstone
Estimated reading time: 4 hours
- [ ] Required -- Search Service Blueprint (40 min)
- [ ] Required -- Analytics Pipeline Blueprint (40 min)
- [ ] Required -- Feature Flag Blueprint (35 min)
- [ ] Optional -- Chat Service Blueprint (35 min)
- [ ] Optional -- Notification Service Blueprint (35 min)
What You Will Be Able to Do After This Path
- Implement and evaluate classical ML algorithms (30 pages of foundations)
- Build and train deep learning models (25 pages of architectures)
- Integrate LLMs with LangChain, LangGraph, and LlamaIndex
- Design and build RAG pipelines with vector databases
- Fine-tune models with LoRA and evaluate with custom benchmarks
- Implement AI guardrails for content safety and hallucination prevention
- Serve models at scale on GPU Kubernetes clusters
- Build end-to-end AI testing and monitoring pipelines
Cross-References to Related Paths
- ML/DL Engineer Path -- Deep dive into DL architectures and research
- Data Scientist Path -- Math foundations, EDA, and statistical modeling
- Data Engineer Path -- Data pipelines that feed ML systems
- Backend Engineer Path -- APIs and infrastructure for AI products
- Platform Engineer Path -- GPU infrastructure and model serving platforms
Total Progress
This path contains approximately 120 pages (30 ML + 25 DL + 25 AI engineering + 40 infrastructure/blueprints). Budget 12 weeks at 5 hours per week. The ML + DL foundations (weeks 1-5) are essential before diving into LLM integration.