A guide to MLOps

A simple yet complete guide to MLOps tools and practices - from a conventional way to a modern approach of working with ML projects. Website available at https://mlops.swiss-ai-center.ch.

Overview

The repository is organized into several branches, each fulfilling a specific role:

• main: the guide that is continuously deployed
• code: the code used to create and train the model referenced in the guide
• data: the dataset used to train and evaluate the model
• extra-data: the supplementary dataset used for inference and labeling before retraining the model
• freeze: a backup of the validated dependencies list which can be used as a fallback in case of breakage in the transitive dependencies tree.

Temporary branches may also exist for ongoing issues and improvements to the guide.

Development

Local development with Docker Compose (recommended)

To improve the documentation locally, run Material for MkDocs with the following commands:

# Build the Docker container
docker compose build

# Start the Docker container
docker compose up serve

You can now access the local development server at http://localhost:8000.

If you make changes to the documentation, the web page should reload.

Local development with Python

To improve the documentation locally, run Material for MkDocs with the following commands:

# Install all dependencies for Material for MkDocs
sudo apt install --yes \
    libcairo2-dev \
    libfreetype6-dev \
    libffi-dev \
    libjpeg-dev \
    libpng-dev \
    libz-dev

# Create the virtual environment
python3.12 -m venv .venv

# Activate the virtual environment
source .venv/bin/activate

# Install the Python dependencies
pip install \
    --requirement requirements.txt \
    --requirement requirements-freeze.txt

# Run Material for MkDocs
mkdocs serve

You can now access the local development server at http://localhost:8000.

If you make changes to the documentation, the web page should reload.

Format the documentation with Docker Compose (recommended)

To format the Markdown documentation, run mdwrap with the following commands:

# Build the Docker container
docker compose build

# Start the Docker container
docker compose up format

Format the documentation with Python

To format the Markdown documentation, run mdwrap with the following commands:

# Create the virtual environment
python3.12 -m venv .venv

# Activate the virtual environment
source .venv/bin/activate

# Install the Python dependencies
pip install \
    --requirement requirements.txt \
    --requirement requirements-all.txt

# Run mdwrap
mdwrap --fmt docs