domain-ml

# Machine Learning Domain

> **Layer 3: Domain Constraints**

## Domain Constraints → Design Implications

| Domain Rule | Design Constraint | Rust Implication |
|-------------|-------------------|------------------|
| Large data | Efficient memory | Zero-copy, streaming |
| GPU acceleration | CUDA/Metal support | candle, tch-rs |
| Model portability | Standard formats | ONNX |
| Batch processing | Throughput over latency | Batched inference |
| Numerical precision | Float handling | ndarray, careful f32/f64 |
| Reproducibility | Deterministic | Seeded random, versioning |

---

## Critical Constraints

### Memory Efficiency

```
RULE: Avoid copying large tensors
WHY: Memory bandwidth is bottleneck
RUST: References, views, in-place ops
```

### GPU Utilization

```
RULE: Batch operations for GPU efficiency
WHY: GPU overhead per kernel launch
RUST: Batch sizes, async data loading
```

### Model Portability

```
RULE: Use standard model formats
WHY: Train in Python, deploy in Rust
RUST: ONNX via tract or candle
```

---

## Trace Down ↓

From constraints to design (Layer 2):

```
"Need efficient data pipelines"
    ↓ m10-performance: Streaming, batching
    ↓ polars: Lazy evaluation

"Need GPU inference"
    ↓ m07-concurrency: Async data loading
    ↓ candle/tch-rs: CUDA backend

"Need model loading"
    ↓ m12-lifecycle: Lazy init, caching
    ↓ tract: ONNX runtime
```

---

## Use Case → Framework

| Use Case | Recommended | Why |
|----------|-------------|-----|
| Inference only | tract (ONNX) | Lightweight, portable |
| Training + inference | candle, burn | Pure Rust, GPU |
| PyTorch models | tch-rs | Direct bindings |
| Data pipelines | polars | Fast, lazy eval |

## Key Crates

| Purpose | Crate |
|---------|-------|
| Tensors | ndarray |
| ONNX inference | tract |
| ML framework | candle, burn |
| PyTorch bindings | tch-rs |
| Data processing | polars |
| Embeddings | fastembed |

## Design Patterns

| Pattern | Purpose | Implementation |
|---------|---------|--