Business problem

Q: How deeply does Delivering Machine Learning Projects: From design to deployment cover business problem?

Refines the problem through descriptions, story backlogs, user validation, performance requirements, UX designs, and story cards, but not complete business mastery.

As explained in Delivering Machine Learning Projects: From design to deployment

A business problem is an organizational difficulty, unmet need, or opportunity that a solution is intended to address.

A business problem is an organizational difficulty, unmet need, or opportunity that a solution is intended to address. Refinement makes project work efficient, directed, and purposeful by connecting organizational issues to application and model requirements. section 5.3

Refine the problem through explicit project artifacts:

Create a business application description and formalize the user story backlog. section 5.3
Validate the application description and stories with users and identify ML model performance requirements. section 5.3
Use UX designs and story cards to document stakeholders, organizational impact, and business priorities. section 5.3

Table 5.3 The contents of a story card for an ML project. Note the final five rows, which establish how to go about creating this. (view table figure)

Item	Notes (example)
Concept	A simple explanation of the top-level area the project addresses; for example, customer service or inventory management.
List of stakeholders	Jo Bloggs (IT), Sam Smith (Proc), Arthur Aske (Logistics)
Business priorities	The list of focus areas for the business: customer service uplift, revenue, cost reduction, growth, diversification, capital efficiency, cash flow, competition, social responsibility, for example.
Impact statement for business	How the solution delivers value; what problem it solves for the customer. For example, this analysis provides insights of interest to the Primary Products division and. . . .
Impact to business priorities	States the link between the impact statement and the business priorities of the company. For example, these insights inform purchasing and manufacturing how to better drive revenue and improve capital efficiency.
Data resources	Identifies the data resources the system will draw on, both for training and for production. Describes how the data will get to the model in production.
Model concept	Describes the model to be used, what it does, and why it’s expected to work.
Functional/nonfunctional requirements	Relates how good the model must be at its job. How much throughput, latency, and availability for the model? How much does serving it cost?
How it will be used	Explains how the effect delivered will be materialized as a business activity. What will be done with the information from the model or the decisions from the system? Also provides information via visualization and a dashboard for decision makers.
Problems and issues	Notes factors that could derail the implementation of the system or prevent it from being useful.

Use the story-card template to connect the project concept with stakeholders, priorities, expected business impact, and the conversations needed to validate it. section 5.3

How Delivering Machine Learning Projects: From design to deployment covers it

Cover of Delivering Machine Learning Projects: From design to deployment

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Also explained in

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Data Without LabelsA business problem is a defined organizational objective or challenge that analysis or a data science project seeks to address.
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Collaborative Software Design: How to facilitate domain modeling decisionsA business problem is a need, objective, task, process, constraint, or challenge affecting users, stakeholders, or an organization.
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BDD in Action, Second EditionA business need is a required improvement or capability arising from an organization's goals or difficulties.

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tooladdressed by · A tool is an instrument or software capability that enables a person or system to perform a task, obtain information, or interact with resources.
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constraintcontrasts with · A constraint is a condition or rule that restricts the permissible states, values, actions, or solutions in a system or problem.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.