8. Compass: Composable & Scalable Signal Engineering, Justin Young, Abnormal Security

Use case: stop all email attacks
- Need high recall
- High precision
- Rapid iteration cycle, because attackers update their techniques
Mantra: move fast and don’t break things
How to build an ML platform?
- Prevent entanglement
- Prevent online / offline skew
Entanglement
- “Changing anything changes everything”
  
  Hard to hard signal and basically impossible to remove ones, with an effect anywhere later on.
- Function composition: f(x) = h(x, g(x))
  
  Function composition are the primary responsibility of ML pipeline
- Unconstrained function composition is the root of entanglement
  
  Total dependencies grow square with the total of signals
  
  So we need constrained function composition instead
- How does the signal dag work in production?
```
@compass.single_output_transformation("variable_len")
def extract_len(input_variable: str) -> int:
	return len(input_variable)
```
- Key features of dag:
  - Data structure append-only
  - Easy to understand the pipeline: list dependencies and viz
  - Validate properties of the pipeline: no cycle, unused branch, every signal produced exactly by one transformation
  - Platformize pipeline: propagate errors downstream, parallel execution of parallel Dags
How to execute this offline/online at scale?
- Most of the time offline and online transformation will use the same transformations
Key takeaway: make sure your ML pipeline implements both a feature store and a function store!

The system should be function centric