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Generic Envisioning Summary

Purpose of this template

This is an example of an envisioning summary completed after envisioning sessions have concluded. It summarizes the materials reviewed, application scenarios discussed and decided, and the next steps in the process.

Summary of Envisioning

Introduction

This document is to summarize what we have discussed in these envisioning sessions, and what we have decided to work on in this machine learning (ML) engagement. With this document, we hope that everyone can be on the same page regarding the scope of this ML engagement, and will ensure a successful start for the project.

Materials Shared with the team

List materials shared with you here. The list below contains some examples. You will want to be more specific.

  1. Business vision statement

  2. Sample Data

  3. Current problem statement

Also discuss:

  1. How the current solution is built and implemented

  2. Details about the current state of the systems and processes.

Applications Scenarios that Can Help [People] Achieve [Task]

The following application scenarios were discussed:

Scenario 1:

Scenario 2:

Add more scenarios as needed

For each scenario, provide an appropriately descriptive name and then follow up with more details.

For each scenario, discuss:

  1. What problem statement was discussed

  2. How we propose to solve the problem (there may be several proposals)

  3. Who would use the solution

  4. What would it look like to use our solution? An example of how it would bring value to the end user.

Selected Scenario for this ML Engagement

Which scenario was selected?

Why was this scenario prioritised over the others?

Will other scenarios be considered in the future? When will we revisit them / what conditions need to be met to pursue them?

More Details of the Scope for Selected Scenario

  1. What is in scope?

  2. What data is available?

  3. Which performance metric to use?

  4. Bar of performance metrics

  5. What are deliverables?

What’s Next?

State documents and timeline

Responsible AI Review

Plan when to conduct a responsible AI process. What are the prerequisites to start this process?

Data Exploration Workshop

A data exploration workshop is planned for DATE RANGE. This data exploration workshops will be X-Y days, not including the time to gain access resources. The purpose of the data exploration workshop is as follows:

  1. Ensure the team can access the data and compute resources that are necessary for the ML feasibility study

  2. Ensure that the data provided is of quality and is relevant to the ML solution

  3. Make sure that the project team has a good understanding of the data

  4. Make sure that the SMEs (Subject Matter Experts) needed are present for Data Exploration Workshop

  5. List people needed for the data exploration workshop

ML Feasibility Study till [date]

Objectives

State what we expect to be the objective in the feasibility study

Timeline

Give a possible timeline for the feasibility study

Personnel needed

What sorts of people/roles are needed for the feasibility study?

What’s After ML Feasibility Study

Detail here

Summary of Timeline

Below is a high-level summary of the upcoming timeline:

Discuss dates for the data exploration workshop, and feasibility study along with any to-do items such as starting responsible AI process, identifying engineering resources. We suggest using a concise bulleted list or a table to easily convey the information.

Last update: March 21, 2022