Simple Implementation of Rendezvous Architecture for Machine Learning Service

Blog post: https://blog.munhou.com/2022/02/09/Simple-Implementation-of-Rendevzous-Architecture-for-Machine-Learning-Services/#more

Architecture Diagram

How to Use

1. Install docker and docker compose.

2. Create conda environment

   conda env create -f env_mac.yaml
   conda activate rendezvous_arch

3. Train canary and production model

   make train-canary

   make train-model

4. Deploy services

   make up

   Wait for several minutes until all services are ready.

5. Make an HTTP-GET request to http://localhost:8000/ping, should get

   {"ping": "pong"}

6. Make an HTTP-POST request to localhost:8000/predict with body:

   {
      "sepalLength": 9,
      "sepalWidth": 10,
      "petalLength": 11,
      "petalWidth": 12
   }

   should get

   {
       "timestamp": 1644370787.021771,
       "messageId": "cec42337-49ad-4f19-a478-69a1ef480e8a",
       "modelInput": {
           "sepalLength": 9.0,
           "sepalWidth": 10.0,
           "petalLength": 11.0,
           "petalWidth": 12.0
       },
       "modelResult": {
           "modelName": "dt",
           "result": 2
       }
   }

7. Create indices for decoy-log and score-log at Kibana http://localhost:5601.