ethics

This guide provides a comprehensive overview of the machine learning (ML) project lifecycle, designed to align stakeholder expectations with the realities of ML development. Key takeaways include:

Iterative, Not Linear: ML projects are cyclical and involve continuous refinement. Early stages are often revisited as the project evolves.

Data is Foundational: A significant portion of project effort, typically 60-70%, is dedicated to data collection, cleaning, and feature engineering. The quality of the data directly determines the success of the model.

Early Feasibility is Crucial: A preliminary study can de-risk projects by validating the approach and identifying data gaps before major resource commitment.

Success is a Partnership: Clear communication, defined business metrics, and stakeholder involvement at each stage are critical for achieving project goals.