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:

Phase	Documentation
Inspiration / Problem Identification	Business Objectives, Business Success Criteria (Define experiment(s) to validate hypothesis)
	Requirements, Assumptions, and Constraints
	Risks and Contingencies Terminology
	Costs and Benefits
	Data Mining Goals and Data Mining Success Criteria
	Project Plan, Initial Assessment of Tools and Technique
EDA and Data Engineering	Data Exploratory Report and Data Description Report
	Data Quality Assessment Report
	Data strategy (Rationale for Inclusion / Exclusion) for the model
	Data engineering design(cleansing, transformation rules)
Feature Engineering & ML Models	Algorithm cheat sheet: show algorithms for different use cases (Document selection and reason)
	Link farm to research papers and relevant external resources
	Test Design and evaluation criteria
	Model evaluation and approval for validating hypothesis
	Assessment of Data Mining Results w.r.t. Business Success
	Actual Code
Operationalise	Continous Monitoring and Maintenance Plans
	Troubleshooting guide for performance and testing techniques
	MLOps design
	Operations Guide and Configuration scripts for API

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.