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AI Engineering
AI Engineering Overview
AI Engineering Landscape
Prompt Engineering
RAG Engineering
Model Development (PyTorch)
Data Engineering
Model Adaptation
Inference Engineering
MLOps
LLMOps
Evaluation & Monitoring
Safety & Governance
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Table of Contents
1. What Is AI Engineering
1.1 AI Engineering vs ML Research
1.2 AI Engineer Skill Stack
2. ML Lifecycle
2.1 Traditional ML Lifecycle
2.2 Changes in the LLM Era
2.3 AI Engineering Pipeline
3. MLOps vs LLMOps
3.1 MLOps Overview
3.2 Specifics of LLMOps
3.3 Tool Ecosystem
4. Key Challenges
4.1 Reproducibility
4.2 Scalability
4.3 Monitoring & Observability
4.4 Cost Control
4.5 Security and Governance
5. AI Engineering Maturity Model
Level 0: Manual Experimentation
Level 1: Basic Automation
Level 2: Standardized Processes
Level 3: Full Automation
Level 4: Continuous Optimization
6. Practical Recommendations
6.1 Getting Started
6.2 Team Building
6.3 Technology Selection Principles
7. Summary
References
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