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MLOps Course: Prometheus & Grafana - Student

This repository serves as a starting point for the hands-on exercise of the DataScientest MLOps course. Its goal is to guide you through setting up a monitoring infrastructure for a machine learning application using Prometheus and Grafana.

The Goal

In the files of this repository, you will find the basic structure needed to get started. Your task will be to complete the files to:

  • Containerize the API and the evaluation service.
  • Define and orchestrate all services (the API, Prometheus, Grafana, etc.) in docker-compose.yml.
  • Automate common tasks with a Makefile.
  • Add the necessary configurations so that Prometheus can collect metrics from the application and Grafana can visualize them.

Project Structure

The repository's structure is intentionally simple to allow you to build it step by step during the course.

├── deployment
│   ├── prometheus/
│   │   └── prometheus.yml
├── src/
│   ├── api/
│   │   ├── Dockerfile
│   │   ├── main.py
│   │   └── requirements.txt
│   ├── evaluation/
│   │   ├── data/
│   │   │   └── News_Category_Dataset_v3.json
│   │   ├── Dockerfile
│   │   ├── requirements.txt
│   │   └── run_evaluation.py
├── docker-compose.yml
└── Makefile
  • deployment/ : This folder will host the configuration files for your monitoring tools.
  • src/ : This folder contains the source code for the API and the evaluation service that you will need to use.
  • docker-compose.yml : This file is to be completed to define all your services.
  • Makefile : This file is to be completed to include your automation commands.

This repository is your workspace. Follow the course instructions to complete it and build your own MLOps infrastructure.