deployml

A CLI tool that deploys a production MLOps stack on GCP with a single command. Built for academic ML courses so students can focus on building models, not infrastructure.

What you get

• MLflow — experiment tracking, artifact storage, and model registry (backed by Cloud SQL Postgres + GCS)
• FastAPI — model serving endpoint that loads the latest registered model from MLflow automatically
• Grafana — monitoring dashboard connected to your metrics database
• BigQuery — mlops dataset with tables for features, predictions, ground truth, and drift metrics

All running on GCP Cloud Run — no servers to manage, scales to zero when idle.

Quick Start

1. Install

pip install deployml-core

2. Initialize your GCP project (enables APIs, creates Artifact Registry)

deployml init --provider gcp --project-id YOUR_PROJECT_ID

3. Configure

cp config.example.yaml config.yaml
# Edit config.yaml and set your project_id

4. Build images

deployml build-images --create-repo

5. Deploy

deployml deploy --verbose

First deploy takes ~20 minutes (Cloud SQL provisioning). Subsequent deploys are 1–2 minutes.

6. Get your URLs

deployml get-urls

Prints service URLs and writes a .env file with MLFLOW_URL, FASTAPI_URL, GRAFANA_URL, BIGQUERY_PROJECT, and BIGQUERY_DATASET.

End-to-End Example

Once deployed, the example/ directory walks through a complete MLOps workflow using a synthetic housing price dataset:

pip install mlflow scikit-learn pandas numpy google-cloud-bigquery db-dtypes python-dotenv requests

python example/scripts/01_load_training_data.py   # load 500 rows into BigQuery
python example/scripts/02_train_model.py           # train RandomForest, log to MLflow
python example/scripts/03_register_model.py        # register model as Production
python example/scripts/04_make_predictions.py      # serve 50 predictions via FastAPI
python example/scripts/05_generate_ground_truth.py # simulate actual outcomes
python example/scripts/06_compute_drift_metrics.py # compute feature drift + MAE
python example/scripts/07_setup_grafana.py         # provision monitoring dashboard

See example/README.md for details.

Teardown

deployml destroy

Deletes all Cloud Run services, Cloud SQL, GCS bucket, and BigQuery dataset. Does not delete Artifact Registry images or the GCP project.

Full Tutorial

See docs/tutorials/gcp-cloud-run.md for a step-by-step walkthrough.

Requirements

• Python 3.10+
• gcloud CLI (authenticated)
• Docker (running)
• Terraform