Welcome to the 2024 MLOps course repository at TPS. This project aims to teach you how to implement:

• A comprehensive end-to-end MLOps pipeline.
• Data lake management using MinIO.
• Pipeline orchestration with ZenML.
• Experiment tracking with MLFlow.
• Model deployment using Hugging Face.

Please feel free to ask questions, as this project involves numerous skills that can sometimes be challenging to master.

For this project, we'll be using Python 3.11: https://www.linuxcapable.com/how-to-install-python-3-11-on-ubuntu-linux/

Install Docker: https://docs.docker.com/engine/install/ubuntu/

We highly recommend using PyCharm (Community or Professional Edition) for this course: https://www.jetbrains.com/fr-fr/pycharm/download/other.html

Install Poetry by following this guide: https://medium.com/@mronakjain94/comprehensive-guide-to-installing-poetry-on-ubuntu-and-managing-python-projects-949b49ef4f76

Then, activate the Poetry environment:

#path/to/this/project
poetry install
poetry shell

In PyCharm settings, navigate to Black and enable the code formatter on code reformat and on save as shown below:

If you haven't already, create your accounts at:

• Discord: https://discord.com/register
• GitHub: https://github.com/join

First, go to src/config cand copy-paste the .env.dist file, rename it .env:

cp src/config/.env.dist src/config/.env

You can then fill all the <fill-here> tokens appropriatly, the last one (HUGGINGFACE_TOKEN) is a personnal access token you can create here. It will be used when deploying our trained model on HuggingFace.

Now, let's start the local stack. First, we need to create the mlflow schema for MLflow to register our metadata store.

#path/to/this/project

# Start the mysql container
docker-compose up mysql-db

# Connect to it
docker exec -ti mlops-tps-2024-mysql-db-1 /bin/bash
mysql -u root -p

# Type your root password

# Create the mlflow database
create database mlflow;

# Finally, exit mysql
exit

# And the container
exit

You can now run the complete stack and register the ZenML stack:

#path/to/this/project
docker-compose up

# Setup your ZenML stack
bash stack/setup-local-stack.sh

Finally, create an mlflow bucket on MinIO.

• Access ZenML Dashboard: https://localhost:8080/
• Access MinIO S3 bucket: https://localhost:9000/
• Access MLFlow Dashboard: https://localhost:5001/

Various tools are used to construct this pipeline. Here are some resources for further reading:

• ZenML: https://docs.zenml.io/getting-started/introduction (watch out, use the up-to-date documentation)
• MLFlow documentation: https://mlflow.org/docs/latest/index.html
• For any questions, feel free to reach out to me on Discord or via email at [email protected].