MLOps Batch Job — Rolling-Mean Signal Pipeline

A minimal MLOps-style batch job that loads OHLCV data, computes a rolling-mean signal, and writes structured metrics + logs. Runs locally or inside Docker with a single command.

Requirements

• Python 3.9+
• Dependencies: numpy, pandas, PyYAML

pip install -r requirements.txt

Local Run

python run.py --input data.csv --config config.yaml --output metrics.json --log-file run.log

CLI Arguments

Argument	Description
--input	Path to OHLCV CSV file (must contain a close column)
--config	Path to YAML config file
--output	Path for output metrics JSON
--log-file	Path for log file

Config Format (config.yaml)

seed: 42
window: 5
version: "v1"

Docker

docker build -t mlops-task .
docker run --rm mlops-task

The container includes data.csv and config.yaml, runs the pipeline, and prints the final metrics JSON to stdout. Exit code is 0 on success, non-zero on failure.

Example Output

metrics.json

{
  "version": "v1",
  "rows_processed": 9996,
  "metric": "signal_rate",
  "value": 0.4991,
  "latency_ms": 70,
  "seed": 42,
  "status": "success"
}

rows_processed = total rows minus warm-up rows (window - 1). With 10,000 rows and window=5, that's 9,996 valid rows.

Error output

{
  "version": "v1",
  "status": "error",
  "error_message": "Required column 'close' not found.",
  "latency_ms": 5
}

Running Tests

python -m pytest tests/ -q

Project Structure

run.py          # Single-file pipeline (validator + processor + reporter + CLI)
config.yaml     # Job configuration
data.csv        # 10,000-row OHLCV dataset
requirements.txt
Dockerfile
metrics.json    # Sample output from a successful run
run.log         # Sample log from a successful run
tests/
  test_validator.py
  test_processor.py
  test_reporter.py
  test_integration.py
  conftest.py