MLops Poc Project

This project is a proof-of-concept (PoC) for MLops (Machine Learning Operations). It utilizes the Brazilian Credit Card Spending dataset from Kaggle. The goal of this project is to demonstrate the implementation of MLops principles and practices in a real-world scenario.

Key Features:

• Data preprocessing: The dataset is preprocessed to handle missing values, outliers, and feature engineering.
• Model training: Various machine learning algorithms are trained and evaluated to find the best performing model.
• Model deployment: The selected model is deployed using a docker.
• Monitoring and maintenance: In theory (given new data), the deployed model can continuously monitored for performance and retrained periodically to ensure accuracy.

This project serves as a starting point for implementing MLops practices in credit expense category prediction. It provides a framework for handling data, training models, and deploying them in a production environment.

For more information, refer to the project documentation and codebase.

Take a look at the Kedro documentation to get started.

• Understand business context 00_problem_proposition
• Frame the DS problem 00_problem_proposition
• Explore the data 01_Explore
• Establish: Baseline, metrics and Models 02_Modeling
• Report you results 03_Report_Results
• Design of solution for future pipeline requirements 04_System_Design.md

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

Create a new conda/mamba environment or read the official documentation

conda create -n mlops python=3.10 -y

or

mamba create -n mlops python=3.10 -c conda-force -y

Activate your environment

conda activate mlops

Declare any dependencies in requirements.txt for pip installation.

To install them, run:

pip install -r requirements.txt

More on [official documentation]

kedro package

pip install dist/brazilian_credit_card_spending-0.1-py3-none-any.whl

Setup Kedro Environment

Install Kedro Docker

pip install kedro-docker

Generate a Dockerfile

kedro docker init

Build a Docker image, It will create a Docker image with example:latest name.

kedro docker build

Run a Kedro project in a Docker Environment

kedro docker run

You can run your Kedro project with:

kedro run

Have a look at the files src/tests/test_run.py and src/tests/pipelines/test_data_science.py for instructions on how to write your tests. Run the tests as follows:

pytest

To configure the coverage threshold, look at the .coveragerc file.

To see and update the dependency requirements for your project use requirements.txt. Install the project requirements with pip install -r requirements.txt.

Further information about project dependencies

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: catalog, context, pipelines and session.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r requirements.txt you will not need to take any extra steps before you use them.

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

And if you want to run an IPython session:

kedro ipython

To automatically strip out all output cell contents before committing to git, you can use tools like nbstripout. For example, you can add a hook in .git/config with nbstripout --install. This will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Further information about using notebooks for experiments within Kedro projects.

Further information about building project documentation and packaging your project.