Layer is a Declarative MLOps Platform that empowers Data Science teams to implement end-to-end machine learning with minimal effort. Layer is a managed solution and can be used without the hassle involved in configuring and setting up servers.
Clone this repo to start a new Layer project.
Install Layer:
pip install layer-sdk
Clone this empty Layer Project:
layer clone https://github.com/layerml/empty
This repo contains a Layer project that you can quickly use to bootstrap your machine learning projects.
Note: Use either Python or SQL featuresets template. Layer does not support multiple language featuresets at the moment.
The empty Layer Project has the following files:
|____.layer
| |____project.yaml # Main project configuration file
|____data
| |____dataset
| | |____dataset.yaml # Definition of the source data
| |____features
| | |____sql_featureset # Template for SQL featureset
| | | |____sql_featureset_feature1.sql # SQL file for your feature
| | | |____sql_featureset_feature2.sql # SQL file for your feature
| | | |____dataset.yaml # Definition of your sql featureset
| | |____python_featureset # Template for Python featureset
| | | |____python_featureset_feature1
| | | | |____feature1.py # Python file for your feature
| | | | |____requirements.txt # Environment config file, if required
| | | |____python_featureset_feature2
| | | | |____feature2.py # Python file for your feature
| | | | |____requirements.txt # Environment config file, if required
| | | |____dataset.yaml # Definition of your Python featureset
|____models
| |____model
| | |____model.py # Training directives of your model
| | |____model.yaml # Training directives of your model
| | |____requirements.txt # Environment config file, if required