/example_ml_project

A ML template that shows core MLOPs components: good project structure, tests, build/CI automation, CD to multiple environments, and actual approval workflows

Primary LanguagePython

This is an ML Project

Instructions:

  • clone repository
  • Run make install to install dependencies
  • Run make test to test end-to-end workflow
  • Optional:
  • Run python run.py to execute workflow directly
  • Run uvicorn inference:app --reload --host 0.0.0.0 --port 8001 to serve model on port 8001
  • After serving model (previous command), run python ml_stuff/models/test_api to query endpoint

Overview

Continual helps ML teams bring software engineering practices to ML deployments. This project is a simple example.

  • Start with a basic project structure:

  • Code that trains a model (train.py)

  • Code that tests our model (test_train.py)

  • Script that builds and tests our code (Makefile)

  • CI job to automate build & test on merge (GH Action - main.yml)

  • The second iteration adds inferencing and Continuous Deployment

  • CD job deploys REST API to AWS ECS on pull request

  • REST API for model inferencing with FastAPI (inference.py)

  • Code that tests our API (test_api.py)

  • Dockerfile to containerize our web service

  • CD via AWS ECS (GH Action --> ECS Fargate)

  • Right now - this deployment requires manual review by me

  • To Do:

  • Add multiple environments

  • Add mocks for tests

  • Add orchestration

  • Sagemaker, AzureML, Vertex