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Designing AI Systems for Constrained Environments

7 chapters · 32 lessons

1. 1. Constraint Mapping and Service Requirements

   4 lessons

   Identify deployment constraints and translate user needs into technical requirements for AI systems.

2. 2. Model Selection and Adaptation Strategies

   5 lessons

   Choose and tune models that balance capability with deployment constraints.

3. 3. Retrieval-Augmented Generation Pipelines

   5 lessons

   Build systems that ground model outputs in trusted, updateable knowledge sources.

4. 4. Hybrid and On-Device Deployment Patterns

   5 lessons

   Architect systems that work across cloud, edge, and offline scenarios.

5. 5. Data Pipelines for Low-Resource Languages

   4 lessons

   Collect, clean, and augment training data when standard datasets are unavailable.

6. 6. Monitoring, Escalation, and Reliability Engineering

   5 lessons

   Design feedback loops and safety mechanisms that maintain service quality in production.

7. 7. Case Study: Building a Public Service AI System

   4 lessons

   Apply all concepts to design a complete AI service for a constrained real-world scenario.