A comprehensive reference for the engineering of production AI/ML systems — covering ML system architecture, MLOps, LLMOps, data engineering for ML, and scaling ML systems from prototype to production at scale.
Scope
This project documents the engineering disciplines required to build, deploy, monitor, and maintain AI/ML systems that serve real users in production — bridging the gap between ML research and production reliability.
Knowledge Map
mindmap
root((Large Scale\nAI/ML Systems))
ML Systems Architecture
ML Pipelines
Feature Stores
Model Serving
Training Infrastructure
MLOps
Model Registry
Experiment Tracking
Drift Detection
Retraining Pipelines
A/B Testing
LLMOps
Prompt Engineering
RAG Systems
LLM Evaluation
Fine-Tuning
Data Engineering for ML
Data Collection
Data Preprocessing
Feature Engineering
Data Validation
Scaling ML Systems
Distributed Training
Model Parallelism
Inference Optimization
Serving at Scale
Sections
| # | Section | Description |
|---|---|---|
| 01 | ML Systems Architecture | End-to-end ML system design |
| 02 | MLOps | Operationalizing ML models |
| 03 | LLMOps | Large Language Model operations |
| 04 | Data Engineering for ML | Data pipelines for ML |
| 05 | Scaling ML Systems | Distributed training and inference |