MLProject is a machine learning project that includes data ingestion, data transformation, model training, and model monitoring components. It is designed to be scalable and easy to use.
The project is divided into several components, each responsible for a specific task:
data_ingestion.py
: Responsible for ingesting data.data_transformation.py
: Transforms the ingested data.model_trainer.py
: Trains a machine learning model on the transformed data.model_monitoring.py
: Monitors the performance of the trained model.
.dvc/
: Contains DVC files for data versioning..ebextensions/
: Contains configuration files for AWS Elastic Beanstalk. Thepython.config
file sets the WSGIPath toapplication:application
.artifacts/
: Contains serialized machine learning models and data files.mlruns/
: Contains metadata about MLflow runs.src/
: Contains the source code of the application.
application.py
: The main entry point of the application.Dockerfile
: Used to create a Docker image of the application.requirements.txt
: Lists the Python packages that the project depends on.
To set up the project, you need to install the dependencies listed in the requirements.txt
file. You can do this by running:
pip install -r requirements.txt
The main entry point of the application is application.py. You can run it with:
python application.py
Your project uses DVC for data versioning. The .dvc
directory and .dvcignore
file are used for this purpose.
The .ebextensions
directory contains configuration files for AWS Elastic Beanstalk. The python.config
file sets the WSGIPath to application:application
.
The application.py
file is the main entry point of your application. It imports various components from the src
directory and defines several routes for a Flask application.
The artifacts
directory contains serialized machine learning models (model.pkl
and preprocessor.pkl
) and data files (raw.csv
, test.csv
, train.csv
).
The mlruns
directory contains metadata about MLflow runs. Each run has a unique ID and contains information such as the user who initiated the run, the start and end times, and the location of any artifacts produced during the run.
The Dockerfile is used to create a Docker image of your application.
The requirements.txt
file lists the Python packages that your project depends on.
The src
directory contains the source code of your application. It includes various components such as data ingestion, data transformation, and model training.
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