Task 4.2: Performance Optimization

Task 4.2: Optimize application performance

Skill 4.2.1: Latency optimization

Kiến thức cần nắm:

• Pre-computation cho predictable queries
• Latency-optimized Bedrock models
• Parallel requests cho complex workflows
• Response streaming
• Performance benchmarking

Giải thích chi tiết:

Latency Reduction Techniques:

TTFT (Time to First Token) là metric quan trọng cho user experience:

• Streaming cho phép user thấy response ngay từ token đầu tiên
• Bedrock latency-optimized models giảm TTFT

Parallel Processing Pattern:

Complex Query
    ├── Sub-query 1 → Model A → Result 1
    ├── Sub-query 2 → Model B → Result 2  (parallel)
    └── Sub-query 3 → Model C → Result 3
                                    ↓
                        Aggregate Results → Final Response

Skill 4.2.2: Retrieval performance

Kiến thức cần nắm:

• Index optimization cho vector databases
• Query preprocessing
• Hybrid search với custom scoring

Giải thích chi tiết:

Vector Search Optimization:

Hybrid Search:

Query → Keyword Search (BM25) → Top 20 results
    ↓
Query → Vector Search (semantic) → Top 20 results
    ↓
Merge + Re-rank → Top 5 final results

Skill 4.2.3: Throughput optimization

Kiến thức cần nắm:

• Token processing optimization
• Batch inference strategies
• Concurrent model invocation management

Skill 4.2.4: FM parameter tuning

Kiến thức cần nắm:

• Model-specific parameter configurations
• A/B testing cho improvements
• Temperature, top-k, top-p selection

Parameter Guidelines:

Skill 4.2.5: Resource allocation

Kiến thức cần nắm:

• Capacity planning cho token processing
• Utilization monitoring
• Auto-scaling cho GenAI traffic patterns

Skill 4.2.6: System performance optimization

Kiến thức cần nắm:

• API call profiling
• Vector database query optimization
• Latency reduction cho LLM inference
• Efficient service communication patterns

Giải thích chi tiết:

End-to-End Optimization Checklist:

1. Profile API calls với X-Ray
2. Identify bottlenecks (FM latency vs retrieval vs processing)
3. Optimize retrieval (index tuning, caching)
4. Optimize FM calls (model selection, parameter tuning)
5. Optimize post-processing (parallel, async)
6. Monitor và iterate

Tài liệu tham khảo

• AWS Blog - Optimize latency for GenAI
• Amazon OpenSearch Vector Search
• Amazon Bedrock Inference Parameters