Peter Fad

AI Engineer: The Ultimate Roadmap ├── 01 Programming Foundations │ ├── Python, NumPy & Pandas │ ├── Data Structures & Algorithms │ ├── Git, Linux & Command Line │ └── Testing, Debugging & Clean Code │ ├── 02 Mathematics & Statistics │ ├── Linear Algebra │ ├── Probability & Statistics │ ├── Calculus & Gradients │ └── Optimization & Loss Functions │ ├── 03 Data Engineering for AI │ ├── Data Collection & APIs │ ├── SQL, Data Modeling & Warehouses │ ├── Data Cleaning & Labeling │ ├── Feature Engineering & Data Quality │ └── Data Pipelines & Versioning │ ├── 04 Machine Learning │ ├── Supervised & Unsupervised Learning │ ├── Regression, Classification & Clustering │ ├── Feature Selection & Model Training │ ├── Scikit-learn & Baseline Models │ └── Cross-Validation & Hyperparameter Tuning │ ├── 05 Deep Learning │ ├── Neural Networks & Backpropagation │ ├── PyTorch or TensorFlow │ ├── CNNs, RNNs & Transformers │ ├── GPU Training & Fine-Tuning │ └── Regularization & Optimization │ ├── 06 LLM & Generative AI │ ├── Tokens, Context Windows & Sampling │ ├── Transformer Architecture & Attention │ ├── Prompt Engineering & Structured Outputs │ ├── Embeddings & Vector Search │ └── RAG (Retrieval-Augmented Generation) │ ├── 07 AI Application Engineering │ ├── LLM APIs & SDKs │ ├── Tool Calling & Function Schemas │ ├── AI Agents & Workflow Orchestration │ ├── MCP & External Integrations │ └── Memory, Guardrails & Human-in-the-Loop │ ├── 08 Evaluation & Experimentation │ ├── Build Representative Evaluation Datasets │ ├── Model Metrics & Error Analysis │ ├── LLM-as-a-Judge & Human Validation │ ├── Hallucination & Safety Testing │ └── A/B Testing & Online Evaluation │ ├── 09 MLOps & Infrastructure │ ├── Docker & Cloud Fundamentals │ ├── Model Serving & Inference APIs │ ├── CI/CD & Infrastructure as Code │ ├── Experiment Tracking & Model Registry │ └── Monitoring, Drift Detection & Retraining │ ├── 10 Production, Security & Optimization │ ├── Authentication & Data Privacy │ ├── Prompt Injection & AI Threat Modeling │ ├── Latency, Throughput & Cost Optimization │ ├── Caching, Batching & Quantization │ └── Logging, Tracing & Failure Recovery │ └── 11 Real-World AI Projects ├── Train & Deploy a Predictive ML Model ├── Build a Document-Based RAG Assistant ├── Create a Tool-Using AI Agent ├── Fine-Tune & Evaluate an Open-Source Model └── Ship an AI Feature with Monitoring & Feedback An AI Engineer doesn't just train models or build demos. They turn data and models into intelligent, reliable, scalable products that solve real-world problems. Learn the fundamentals. Build real systems. Evaluate relentlessly. Deploy to production.