Welcome to the Machine Learning Lifecycle Playbook Repository!
This repository is dedicated to providing information and explanations about best practices for structuring machine learning projects. It contains information on the various stages of the lifecycle such as data collection, preprocessing, model development, testing, and deployment. The goal of this repository is to provide a resource for organizations and individuals looking to standardize their machine learning projects.
The repository includes guidelines on how to properly organize and document each stage of the project, as well as templates for project documentation, such as project proposals and technical requirements. It also includes information on how to evaluate the performance of machine learning models, and best practices for monitoring and maintaining deployed models.
We believe that a standardization of machine learning projects will lead to increased efficiency, higher model performance, and less time to go to production. By following the guidelines in this repository, you'll be able to reduce the risk of errors, inconsistencies and misunderstandings in your projects.
We encourage you to use this repository as a resource when planning and executing your machine learning projects. Feel free to submit pull requests if you have any improvements or suggestions to share with the community.
Thank you for visiting and happy coding!