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End-to-End MLOps Example

This example walks through a complete MLOps workflow using a synthetic housing price dataset. By the end you will have a model trained and registered in MLflow, served via FastAPI, and monitored in Grafana with BigQuery as the data backend.

Prerequisites

  1. Deploy the infrastructure following the GCP Cloud Run tutorial
  2. Run deployml get-urls to generate your .env file
  3. Install Python dependencies:
pip install mlflow scikit-learn pandas numpy google-cloud-bigquery db-dtypes python-dotenv requests

Environment

All scripts read from the .env file written by deployml get-urls. It should contain:

MLFLOW_URL=https://...
FASTAPI_URL=https://...
GRAFANA_URL=https://...
BIGQUERY_PROJECT=your-project-id
BIGQUERY_DATASET=mlops

Scripts

Run in order from the project root (where .env lives):

Step 1 — Load training data into BigQuery

python example/scripts/01_load_training_data.py

Generates 500 rows of synthetic housing data and loads them into the offline_features BigQuery table.

Verify: 

bq query --use_legacy_sql=false 'SELECT COUNT(*) FROM `YOUR_PROJECT.mlops.offline_features`'

Step 2 — Train a model with MLflow

python example/scripts/02_train_model.py

Pulls features from BigQuery, trains a RandomForestRegressor, and logs parameters, metrics, and the model artifact to MLflow.

Verify: open MLFLOW_URL in your browser — you should see the housing-price-prediction experiment with a completed run.

Step 3 — Register the model

python example/scripts/03_register_model.py

Finds the best run by RMSE, registers it as HousingPriceModel, and promotes it to the Production stage.

Verify: in the MLflow UI, click Models → HousingPriceModel → Production stage should be set.

Step 4 — Make predictions

python example/scripts/04_make_predictions.py

Pulls 50 rows from offline_features and sends each to FastAPI /predict. FastAPI loads the model from MLflow on startup and automatically logs each prediction to the predictions BigQuery table.

Verify: 

bq query --use_legacy_sql=false 'SELECT COUNT(*) FROM `YOUR_PROJECT.mlops.predictions`'

Also check FastAPI is serving the model: 

curl FASTAPI_URL/health
# model_loaded should be true

Step 5 — Generate ground truth

python example/scripts/05_generate_ground_truth.py

For each prediction, generates a fake actual value (predicted value + noise) and writes it to the ground_truth table. In a real scenario this would be actual outcomes matched back by entity_id.

Step 6 — Compute drift metrics

python example/scripts/06_compute_drift_metrics.py

Computes feature mean shift (training distribution vs recent data) and MAE (predictions vs ground truth). Writes results to the drift_metrics table.

Step 7 — Set up Grafana dashboard

python example/scripts/07_setup_grafana.py

Provisions a monitoring dashboard in Grafana via the API showing: - Prediction volume over time - Mean predicted price over time - Feature mean shift per feature - MAE over time

Open GRAFANA_URL in your browser (login: admin / admin) to view the dashboard.

Dataset

Synthetic housing data with features:

Feature Description
bedrooms Number of bedrooms (1–5)
bathrooms Number of bathrooms (1–3)
area_sqft Living area in square feet (800–4000)
lot_size Lot size in square feet (2000–10000)
year_built Year the house was built (1960–2022)
city City encoded as integer (0–4)
state State encoded as integer (0–2)

Target: price = area_sqft * 200 + bedrooms * 15000 + bathrooms * 10000 + (2023 - year_built) * -500 + noise