From Data Collection to Model Deployment: Key Deliverables in a Machine Learning Project

Intro:
Documentation is often overlooked while developing software specially if the methodology is agile. Agile emphasizes working software over comprehensive documentation. But if the software development is related to a machine learning problem then documentation to support the development process is key.

Documentation Assets Inventory:

The Building Blocks of a Successful Machine Learning Project: Deliverables and Documentation

1. Machine learning models are built using data, and it's crucial that the data and methods used to build the model can be replicated by others. Documentation helps to ensure that the work done in the project is repeatable and reproducible.
2. Facilitate better collaboration between the team working on the machine learning problem. It also ensures that everyone is on the same page regarding the scope, goals, and methods used in the project.
3. By documenting the data sources, preprocessing steps, modeling techniques, and evaluation metrics, it's easier to make changes and improvements to the model as needed. Documentation helps to make it easier to maintain the machine learning model over time.
4. Documentation makes it easier for stakeholders and users to understand how the model works, what data it's based on, and how accurate it is. Documentation helps to ensure that the machine learning model is transparent and understandable.
5. Documentation helps to ensure that the machine learning project is compliant with relevant regulations and standards

Overall, documentation is an essential part of any machine learning project. It helps to ensure that the project is well-planned, well-executed, and well-documented, and that it can be easily maintained and scaled in the future.