MLFlow Recipes in Titanic Competiton

This repository is learning code for designing a solution using MLFlow Recipes. MLFlow Recipes is an open-source project developed by the MLFlow community to provide a set of pre-built, tested, and well-documented machine learning (ML) workflows or "recipes" that can be easily adapted to different ML tasks. These recipes are built on top of the MLFlow framework, which is an open-source platform for the complete machine learning lifecycle, including data preprocessing, model training, evaluation, and deployment. In the project, we are going to design a solution for competition Titanic - Machine Learning from Disaster.

Reference

• https://mlflow.org/docs/latest/recipes.html
• https://www.kaggle.com/competitions/titanic/

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Code

Download or clone this repository.

Data

1. Download the dataset in Titanic - Machine Learning from Disaster
2. Extract all the files in ./data/input/ folder
3. Now, you can run the code using mlflow recipes!

Running

You can run this code locally or using databricks.

# Create a Python environment
$ python -m venv .venv
$ source .venv/bin/activate

# Install the requirements
$ pip install -r requirements.txt

# Run using: notebooks/jupyter; or
# Run using: notebooks/databricks; or
# Run using: terminal
$ mlflow recipes run --profile local

# Visualize the experiment performance
# Paths according to `profiles/local.yaml`
$ mlflow ui \
$   --backend-store-uri="sqlite:///metadata/mlflow/mlruns.db" \
$   --default-artifact-root="./metadata/mlflow/mlartifacts"

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Also look ~

• License MIT
• Created by leomaurodesenv