Overview
run_feature_worker is the second step of the pipeline. It triggers the job-feature-worker pipeline job, which reads raw OHLCV data from blob storage, computes technical indicators via TA-Lib, and writes the enriched feature dataset back to storage.
The tool blocks until the job completes and returns the output blob URL.
Parameters
Blob storage URL pointing to a
data_extractor_*.json file.This is the output_url returned by run_data_extraction.Feature engineering configuration.
Returns
| Field | Description |
|---|---|
status | Job terminal status (Succeeded) |
output_url | Full HTTPS URL to the output blob — pass to run_ml_job or run_dl_job |
output_name | Blob filename |
execution_name | Job execution ID for audit/debugging |
Example
Resources
| Resource | Value |
|---|---|
| Container Apps Job | job-feature-worker |
| Container name | feature-worker |
| Env vars injected | INPUT_URL, CONFIG |
| Output blob prefix | feature_engine_ |
| Timeout | 600 seconds |
Next Step
Passoutput_url (along with the original data_extractor_ URL) to either:
run_ml_job— classical ML with scikit-learnrun_dl_job— deep learning with PyTorch