Understand why AI applications need specialized CI/CD approaches beyond traditional software.
## Unique Challenges
### Non-Deterministic Outputs ``` Traditional Software: f(input) → deterministic output AI Applications: f(input) → probabilistic output distribution
Challenge: Same test, different results each run Solution: Statistical assertions, confidence intervals ```
### Testing Complexity ```yaml # Traditional test - input: add(2, 3) expected: 5
# AI test - needs fuzzy matching - input: "Summarize this article" expected_contains: ["key point 1", "key point 2"] expected_sentiment: "neutral" expected_length_range: [100, 500] min_semantic_similarity: 0.85 ```
### Version Management ``` Components to Version: ├── Application Code (Git) ├── Model Weights (DVC, MLflow) ├── Prompts (Prompt Registry) ├── Training Data (Delta Lake, DVC) ├── Evaluation Datasets (Version controlled) └── Configuration (Feature flags) ```
## Pipeline Architecture
``` ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ Commit │────▶│ Build │────▶│ Unit Tests │ └─────────────┘ └─────────────┘ └─────────────┘ │ ┌─────────────┐ ┌──────▼──────┐ │ Deploy │◀────│ AI Evals │ │ Staging │ │ (Sample) │ └─────────────┘ └─────────────┘ │ ┌──────▼──────┐ ┌─────────────┐ │ Full Eval │────▶│ Canary │ │ Suite │ │ Deploy │ └─────────────┘ └─────────────┘ ```