Page Summary

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• Machine learning (ML) projects require early and consistent collaboration with stakeholders who have varying levels of involvement and expectations.

• Clearly define project deliverables like design documents, experimental results, and production-ready implementations, aligning with stakeholder expectations for each project phase.

• Proactively communicate the unique complexities and potential challenges inherent in ML projects to manage stakeholder expectations and ensure project success.

• Establish clear communication channels and involve all necessary teams, including those requiring approval, for efficient project execution.

ML projects have multiple stakeholders with varying levels of involvement andresponsibilities. Early involvement and effective collaboration withstakeholders is essential for developing the right solution, managingexpectations, and ultimately for a successful ML implementation.

As early as possible, define your project's stakeholders, the expecteddeliverables, and the preferred communication methods.

Be sure to include them in your list of stakeholders, as well as any otherteams who need to approve aspects of your ML solution.

Deliverables

Each stakeholder might expect different deliverables at each phase of theproject. Here's a list of common deliverables.

• Design doc. Before you write a line of code, you'll most likely create adesign doc that explains the problem, the proposed solution, the potentialapproaches, and possible risks. Typically, the design doc functions as a wayto receive feedback and address questions and concerns from the project'sstakeholders.

• Experimental results. You must communicate the outcomes from theexperimentation phase. You'll typically include the following:

  • The record of your experiments with their hyperparameters and metrics.
  • The training stack and saved versions of your model at certaincheckpoints.

• Production-ready implementation. A full pipeline for training andserving your model is the key deliverable. At this phase, createdocumentation for future engineers that explain modeling decisions,deployment and monitoring specifics, and data peculiarities.

You should align early with your stakeholders on their expectationsfor each phase of the project.

Keep in mind

In some cases, stakeholders might not understand the complexities and challengesof ML. This can make getting projects prioritized and executed difficult. Forexample, some stakeholders might assume that ML is similar to traditionalsoftware engineering practices with deterministic outcomes. They might notunderstand why the project's progress is stalled or why a project's milestonesare non-linear.

To manage stakeholder expectations, it's critical to be clear about thecomplexities, timeframes, and deliverables at each stage of your project.