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Automate Data Onboarding, Validate, and Govern

本课程是 GenAI Deployment & Governance 专项课程的一部分

位教师：John Whitworth

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Systematic metadata analysis maintains data quality and helps control storage costs in large-scale AI environments.
Effective data retention balances regulatory compliance, business requirements, and long-term cost optimization.
Automated data onboarding ensures consistency, quality, and scalability as enterprise data volumes increase.
Proactive data governance prevents downstream issues and accelerates AI development and deployment cycles

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 GenAI Deployment & Governance 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Transform your approach to enterprise data governance in AI-driven environments. In today's data-intensive landscape, organizations struggle with metadata chaos, compliance gaps, and manual onboarding bottlenecks that slow AI innovation. This course empowers ML and AI professionals to tackle these critical challenges head-on.

This Short Course was created to help machine learning and artificial intelligence professionals accomplish systematic data governance that enables scalable AI operations. By completing this course, you'll be able to eliminate data redundancy through systematic metadata analysis, ensure bulletproof compliance with GDPR and industry regulations while optimizing storage costs, and implement automated workflows that transform manual data chaos into streamlined, validated pipelines. By the end of this course, you will be able to: • Analyze metadata catalogs to identify redundant or stale datasets • Evaluate data retention policies for regulatory compliance and storage cost optimization • Create standardized processes to automate data onboarding, validation, and classification This course is unique because it bridges the gap between data governance theory and practical AI operations, providing hands-on experience with real-world tools like DataHub workflows and GDPR compliance frameworks that you'll encounter in enterprise environments. To be successful in this course, you should have a background in data management concepts, basic understanding of regulatory frameworks, and familiarity with enterprise data systems.

单元详情

Learners will master the systematic analysis of enterprise metadata catalogs to identify redundant datasets, assess data staleness, and implement optimization strategies that reduce storage costs while improving data quality.

涵盖的内容

2个视频1篇阅读材料2个作业

Learners will master the systematic evaluation of data retention policies to ensure regulatory compliance while optimizing storage costs through strategic lifecycle management.

涵盖的内容

3个视频2篇阅读材料2个作业

3个视频总计20分钟

GDPR Compliance Failures and Enterprise Risk4分钟
Regulatory Framework Analysis for Data Retention9分钟
Cost Optimization Through Strategic Data Lifecycle Management7分钟

2篇阅读材料总计13分钟

GDPR and Industry-Specific Retention Requirements8分钟
Retention Policy Assessment and Documentation Framework 5分钟

2个作业总计18分钟

Compliance Gap Analysis and Policy Reconciliation Project15分钟
Regulatory Compliance Knowledge Check3分钟

Learners will design and implement comprehensive automated data onboarding processes that ensure consistency, quality, and scalability while reducing manual overhead and accelerating AI development cycles.