Automate Data Onboarding, Validate, and Govern

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

• Perform systematic metadata analysis to maintain data quality and control costs
• Balance data retention with regulatory compliance and business needs
• Automate data onboarding for consistency and scalability
• Implement proactive governance to accelerate AI development cycles

• Basic familiarity with topic and terminology
• Willingness to engage in applied exercises or case studies