Business value in Delivering Machine Learning Projects: From design to deployment

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

Connects value to requirements, data, process integration, output consumption, and risks that can negate usefulness.

Business value is the beneficial organizational impact produced by a product, service, or project outcome.

Business value is the organizational benefit a product, service, or project outcome is intended to deliver. In an ML project, value depends not only on useful predictions but also on where outputs enter a business process and how people consume them. section 5.3

Make the intended value explicit by documenting:

The problem addressed and stakeholders affected. section 5.3
The business priorities supported, such as customer-service improvement, revenue, cost reduction, growth, or cash flow. section 5.3
The impact statement, model constraints, and point in the process where the output is created, received, and used. 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 fields to connect business priorities, expected impact, stakeholders, requirements, data resources, and operational use. section 5.3

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

Also explained in

Related concepts

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.