4 Before you model: Communication and Logistics of Projects · Machine Learning Engineering in Action
Communication strategies and techniques for ensuring that the early stages of an ML project (from planning to prototype of a solution) capture the elements that actually solve a business problem · Determining how to interact with a larger team throughout development to gain feedback and make adjustments through the use of phased planned meetings that focus on relatable demonstrations of the in-progress version of a solution · Why setting limits on research, experimentation, and prototyping are important, as well as how to define what they should be so that you can actually ship your project to production eventually · Reasons why and how to incorporate business rules and restrictions on ML projects to ensure that a code base is flexible to adapt to last-minute unexpected changes that need to be made · How to communicate effectively to a non-technical audience and how to explain what your model can and can’t do to reduce the friction of complex projects and ensure that meetings are shorter and more efficient
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4 Before you model: Communication and Logistics of Projects

This chapter covers

Communication strategies and techniques for ensuring that the early stages of an ML project (from planning to prototype of a solution) capture the elements that actually solve a business problem

Determining how to interact with a larger team throughout development to gain feedback and make adjustments through the use of phased planned meetings that focus on relatable demonstrations of the in-progress version of a solution

Why setting limits on research, experimentation, and prototyping are important, as well as how to define what they should be so that you can actually ship your project to production eventually

Reasons why and how to incorporate business rules and restrictions on ML projects to ensure that a code base is flexible to adapt to last-minute unexpected changes that need to be made

How to communicate effectively to a non-technical audience and how to explain what your model can and can’t do to reduce the friction of complex projects and ensure that meetings are shorter and more efficient

4.1 Communication: defining the problem

4.1.1 Understanding the problem

4.1.2 Critical discussion boundaries

4.2 Don’t waste our time: critical meetings with cross-functional teams

4.2.1 Experimental update meeting (Do we know what we’re doing here?)

4.2.2 SME review (prototype review / can we solve this?)

4.2.3 Development Progress Review(s) (is this thing actually going to work?)

4.2.4 MVP review (did you build what we asked for?)

4.2.5 Pre-prod review (We really hope we didn’t screw this up)

4.3 Setting limits on your experimentation

4.3.1 Set a time limit

4.3.2 Can you put this into production? Would you want to maintain it?

4.3.3 TDD vs RDD vs PDD vs CDD for ML projects

4.4 Business rules chaos

4.4.1 Embracing chaos by planning for it

4.4.2 Human in the loop design

4.4.3 What’s your backup plan?

4.5 How to talk about results