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1What is AI Engineering?
2Research vs Engineering Goals
3The AI Engineer's Toolkit
4Speed vs Novelty Trade-offs
5When to Use Pre-trained Models
6The 80/20 Rule in AI Engineering
7Collaborative Workflows
8Measuring Success in Production
9Layers of the Modern AI Stack
10Foundation Models vs Task-Specific Models
11Model Hosting Options: API vs Self-Hosted
12The Vector Database Layer
13Orchestration Frameworks Overview
14Embedding Models in the Stack
15Observability and Monitoring Tools
16Data Pipeline Infrastructure
17Evaluation and Testing Frameworks
18The Prompt Management Layer
19Deployment and Serving Infrastructure
20Integration Points and APIs
21The Build vs Buy Spectrum
22Evaluating Vendor Lock-in Risk
23Cost Analysis Framework
24Control vs Convenience Trade-offs
25Data Privacy and Compliance Considerations
26Latency and Performance Requirements
27Hybrid Architecture Patterns
28Decision Framework for Model Selection
29Prototyping vs Production Architecture
30Reassessing Architecture Decisions
31Why Cost Matters in AI Systems
32Token Economics and Pricing Models
33Measuring Cost per Request
34Cost vs Performance Trade-offs
35Budget Planning and Forecasting
36Cost Visibility and Tracking Infrastructure
37Quick Wins for Cost Reduction
38Building Cost into Architecture Decisions

1What is AI Engineering?
2Research vs Engineering Goals
3The AI Engineer's Toolkit
4Speed vs Novelty Trade-offs
5When to Use Pre-trained Models
6The 80/20 Rule in AI Engineering
7Collaborative Workflows
8Measuring Success in Production
9Layers of the Modern AI Stack
10Foundation Models vs Task-Specific Models
11Model Hosting Options: API vs Self-Hosted
12The Vector Database Layer
13Orchestration Frameworks Overview
14Embedding Models in the Stack
15Observability and Monitoring Tools
16Data Pipeline Infrastructure
17Evaluation and Testing Frameworks
18The Prompt Management Layer
19Deployment and Serving Infrastructure
20Integration Points and APIs
21The Build vs Buy Spectrum
22Evaluating Vendor Lock-in Risk
23Cost Analysis Framework
24Control vs Convenience Trade-offs
25Data Privacy and Compliance Considerations
26Latency and Performance Requirements
27Hybrid Architecture Patterns
28Decision Framework for Model Selection
29Prototyping vs Production Architecture
30Reassessing Architecture Decisions
31Why Cost Matters in AI Systems
32Token Economics and Pricing Models
33Measuring Cost per Request
34Cost vs Performance Trade-offs
35Budget Planning and Forecasting
36Cost Visibility and Tracking Infrastructure
37Quick Wins for Cost Reduction
38Building Cost into Architecture Decisions

← AI Engineering

Lesson 1617 of 1,886·39. Classical ML Deployment (Non-LLM)Pro lesson

Model Compression for Serving

Apply pruning, quantization, and knowledge distillation to reduce model size and inference latency.

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