利益相关方
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根据您的偏好保存内容并对其进行分类。
机器学习项目涉及多个利益相关方,他们的参与程度和责任各不相同。尽早与利益相关方进行有效协作对于开发合适的解决方案、管理预期以及最终成功实现机器学习至关重要。
尽早确定项目的利益相关者、预期交付内容和首选沟通方式。
请务必将他们以及需要批准机器学习解决方案相关事宜的任何其他团队纳入利益相关方名单中。
交付成果
在项目的每个阶段,每位利益相关者可能期望获得不同的交付内容。下面列出了一些常见的交付内容。
设计文档。在编写任何代码之前,您很可能会先创建一份设计文档,其中说明问题、建议的解决方案、潜在的方法和可能的风险。通常,设计文档可用于接收反馈并解决项目利益相关者提出的问题和疑虑。
实验结果。您必须传达实验阶段的结果。您通常需要添加以下内容:
- 实验记录,其中包含超参数和指标。
- 训练堆栈以及模型在特定检查点处的已保存版本。
可用于生产环境的实现。用于训练和部署模型的完整流水线是关键的可交付成果。在此阶段,请为未来的工程师创建文档,其中说明了模型构建决策、部署和监控细节以及数据特性。
您应尽早与利益相关方就项目每个阶段的预期达成一致。
注意事项
在某些情况下,利益相关者可能不了解机器学习的复杂性和挑战。这可能会导致难以确定项目优先级并执行项目。例如,某些利益相关者可能会认为机器学习与具有确定性结果的传统软件工程实践类似。他们可能不了解项目进度停滞不前的原因,也不了解项目里程碑为何是非线性的。
为了管理利益相关者的预期,您必须清楚了解项目每个阶段的复杂性、时间范围和交付内容。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-07-27。
[null,null,["最后更新时间 (UTC):2025-07-27。"],[[["\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."]]