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Feedback

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

Feedback is information, opinions, or evaluations provided about work, behavior, or outcomes to support assessment and improvement.

Feedback turns users’ and team members’ opinions, observations, and evaluations into evidence for correcting systems and improving delivery. In production, a usable mechanism matters only when people are encouraged to use it; after delivery, structured and respectful collection makes problems discussable. section 9.5 section 10.3

Apply feedback as an explicit project and operational practice:

  • Provide a confidential or otherwise appropriate channel for users to report performance and usability problems. section 9.5
  • Ask users directly for their opinions instead of assuming that a passive channel will produce useful reports. section 9.5
  • Collect team responses through a review form, choosing open or anonymous participation according to team dynamics. section 10.3
  • Assemble findings, present the issues, and use them to discuss concrete improvements without enabling destructive contributions. section 10.3

Slow complaint-monitoring system

Call-center operatives privately report that a complaint-monitoring system times out during login. Investigation finds backend queries running for several minutes because the production database was not configured and indexed as intended. User feedback exposes a deployment defect that testing failed to reveal. section 9.5

Table 10.3 An example post-project review form for feedback from the team. Rate from 1-5, where 1 equals they strongly disagree and 5 equals they strongly agree. (view table figure)

Questions

Rating (1-5)

Notes

The purpose and direction of the project was clear to me when I started work on it.

The resources were sufficient.

The timeline was realistic.

We worked together well as a team.

We worked well with the client.

The result of the project was excellent.

What was the best technical element in the project?

What didn’t work?

What could we do better?

What is the one change that you would make?

Use the post-project review form to rate project clarity, resources, timeline, teamwork, client collaboration, and results, then record what worked and what should change. section 10.3

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

How deep it goes
Explains collection channels, participation methods, review forms, presentation, and investigation, but stops short of formal feedback analytics.
Examples
Uses confidential user complaints about login timeouts and a rated team post-project review form.
What it assumes
Assumes participation in system delivery or team work while teaching feedback collection, confidentiality, and constructive review from practice.
What you can do after
Operate feedback channels and review practices to diagnose production defects and select improvements.
How it connects
Connects feedback with logging, monitoring, alerting, system management, production deployment, team dynamics, and post-project review.

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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.