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ML projects have multiple stakeholders with varying levels of involvement and
responsibilities. Early involvement and effective collaboration with
stakeholders is essential for developing the right solution, managing
expectations, and ultimately for a successful ML implementation.
As early as possible, define your project's stakeholders, the expected
deliverables, and the preferred communication methods.
Be sure to include them in your list of stakeholders, as well as any other
teams who need to approve aspects of your ML solution.
Deliverables
Each stakeholder might expect different deliverables at each phase of the
project. Here's a list of common deliverables.
Design doc. Before you write a line of code, you'll most likely create a
design doc that explains the problem, the proposed solution, the potential
approaches, and possible risks. Typically, the design doc functions as a way
to receive feedback and address questions and concerns from the project's
stakeholders.
Experimental results. You must communicate the outcomes from the
experimentation 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 certain
checkpoints.
Production-ready implementation. A full pipeline for training and
serving your model is the key deliverable. At this phase, create
documentation for future engineers that explain modeling decisions,
deployment and monitoring specifics, and data peculiarities.
You should align early with your stakeholders on their expectations
for each phase of the project.
Keep in mind
In some cases, stakeholders might not understand the complexities and challenges
of ML. This can make getting projects prioritized and executed difficult. For
example, some stakeholders might assume that ML is similar to traditional
software engineering practices with deterministic outcomes. They might not
understand why the project's progress is stalled or why a project's milestones
are non-linear.
To manage stakeholder expectations, it's critical to be clear about the
complexities, timeframes, and deliverables at each stage of your project.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eMachine learning (ML) projects require early and consistent collaboration with stakeholders who have varying levels of involvement and expectations.\u003c/p\u003e\n"],["\u003cp\u003eClearly define project deliverables like design documents, experimental results, and production-ready implementations, aligning with stakeholder expectations for each project phase.\u003c/p\u003e\n"],["\u003cp\u003eProactively communicate the unique complexities and potential challenges inherent in ML projects to manage stakeholder expectations and ensure project success.\u003c/p\u003e\n"],["\u003cp\u003eEstablish clear communication channels and involve all necessary teams, including those requiring approval, for efficient project execution.\u003c/p\u003e\n"]]],[],null,["# Stakeholders\n\nML projects have multiple stakeholders with varying levels of involvement and\nresponsibilities. Early involvement and effective collaboration with\nstakeholders is essential for developing the right solution, managing\nexpectations, and ultimately for a successful ML implementation.\n\nAs early as possible, define your project's stakeholders, the expected\ndeliverables, and the preferred communication methods.\n\nBe sure to include them in your list of stakeholders, as well as any other\nteams who need to approve aspects of your ML solution.\n\nDeliverables\n------------\n\nEach stakeholder might expect different deliverables at each phase of the\nproject. Here's a list of common deliverables.\n\n- **Design doc.** Before you write a line of code, you'll most likely create a\n design doc that explains the problem, the proposed solution, the potential\n approaches, and possible risks. Typically, the design doc functions as a way\n to receive feedback and address questions and concerns from the project's\n stakeholders.\n\n \u003cbr /\u003e\n\n- **Experimental results.** You must communicate the outcomes from the\n experimentation phase. You'll typically include the following:\n\n - The record of your experiments with their hyperparameters and metrics.\n - The training stack and saved versions of your model at certain checkpoints.\n- **Production-ready implementation.** A full pipeline for training and\n serving your model is the key deliverable. At this phase, create\n documentation for future engineers that explain modeling decisions,\n deployment and monitoring specifics, and data peculiarities.\n\nYou should align early with your stakeholders on their expectations\nfor each phase of the project.\n\n### Keep in mind\n\nIn some cases, stakeholders might not understand the complexities and challenges\nof ML. This can make getting projects prioritized and executed difficult. For\nexample, some stakeholders might assume that ML is similar to traditional\nsoftware engineering practices with deterministic outcomes. They might not\nunderstand why the project's progress is stalled or why a project's milestones\nare non-linear.\n\nTo manage stakeholder expectations, it's critical to be clear about the\ncomplexities, timeframes, and deliverables at each stage of your project."]]