MLOps

Cloud agnostic tech stack for starting an MLOps platform (Level 1)

"We'll build a pipeline - after we deploy the model."

Model drift will hit when it's least convenient for you

To run: Make sure docker is running and you have Docker Compose installed.

1. Clone the project

   git clone https://github.com/jmeisele/ml-ops.git

2. Change directories into the repo

   cd ml-ops

3. Run database migrations and create the first Airflow user account.

   docker-compose up airflow-init

4. Build our images and launch with docker compose

   docker-compose pull && docker-compose up

5. Open a browser and log in to MinIO

   user: minioadmin

   password : minioadmin

   Create a bucket called mlflow

   

6. Open a browser and log in to Grafana

   user: admin

   password : admin

   

   Both Promethus and InfluxDB data sources have already been provisioned along with an MLOps Demo Dashboard and a Notification Channel.

7. Add the notification channel to some panels

8. Start the send_data.py script which sends a POST request every 0.1 seconds

9. Open a browser and turn on the Airflow DAG used to retrain our ML model

   user: airflow

   password : airflow

10. Lower the alarm threshold to see the Airflow DAG pipeline get triggered

11. Check MLFlow after the Airflow DAG has run to see the model artifacts stored using MinIO as the object storage layer.

12. (Optional) Send a POST request to our model service API endpoint

    curl -v -H "Content-Type: application/json" -X POST -d
    '{
        "median_income_in_block": 8.3252,
        "median_house_age_in_block": 41,
        "average_rooms": 6,
        "average_bedrooms": 1,
        "population_per_block": 322,
        "average_house_occupancy": 2.55,
        "block_latitude": 37.88,
        "block_longitude": -122.23
    }'
    http://localhost/model/predict

13. (Optional) If you are so bold, you can also simluate production traffic using locust, but keep in mind you have a lot of services running on your local machine, you would never deploy a production ML API on your local machine to handle production traffic.

Level 1 Workflow & Platform Architecture

Model Serving Architecture

Services

• nginx: Load Balancer
• python-model-service1: FastAPI Machine Learning API 1
• python-model-service2: FastAPI Machine Learning API 2
• postgresql: RDBMS
• rabbitmq: Message Queue
• rabbitmq workers: Workers listening to RabbitMQ
• locust: Load testing and simulate production traffic
• prometheus: Metrics scraping
• minio: Object storage
• mlflow: Machine Learning Experiment Management
• influxdb: Time Series Database
• chronograf: Admin & WebUI for InxfluxDB
• grafana: Performance Monitoring
• redis: Cache
• airflow: Workflow Orchestrator
• bridge server: Receives webhook from Grafana and translates to Airflow REST API

gotchas:

Postgres:

Warning: scripts in /docker-entrypoint-initdb.d are only run if you start the container with a data directory that is empty; any pre-existing database will be left untouched on container startup.

✨ Contributors

Thanks goes to these incredible people: