إنشاء كتالوج بيانات لاكتشاف الذكاء الاصطناعي

Enable teams to find and reuse high-value datasets efficiently and securely.

Pillar

Data – Readiness, Governance, Quality & Ethics

ملخص

This course guides participants through designing and implementing a data catalog tailored for AI initiatives. It emphasizes how effective data discovery and reuse can accelerate Generative AI projects while ensuring data governance and quality standards.

Learning Objectives

Participants will be able to:

• Understand the purpose and benefits of a data catalog in AI environments

• Design and implement a data catalog framework for enterprise use

• Enable effective dataset discovery, classification, and metadata management

• Promote data reuse to accelerate AI project delivery

• Ensure data security and compliance within the catalog

Target Audience

• Data architects and engineers

• AI project leads

• Data stewards and governance professionals

• Business analysts involved in AI data strategy

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructional sessions on data catalog principles and tools

• Hands-on exercises in catalog setup and metadata tagging

• Case studies of successful AI data catalogs

• Group workshops to develop catalog design strategies

Materials Provided

• Templates for data catalog structures and metadata schemas

• Guides on catalog best practices and tool recommendations

• Sample data classification and tagging frameworks

Outcomes

• Capability to build and manage a data catalog to support GenAI

• Enhanced collaboration through improved data discoverability

• Stronger data governance embedded in catalog practices

• Accelerated AI project timelines via effective data reuse

Outline / Content

Day 1: Introduction to Data Catalogs and AI Data Discovery

• Importance of data catalogs in AI workflows

• Key components and features of a data catalog

Day 2: Designing a Data Catalog Framework

• Metadata management and dataset classification

• Defining access controls and governance roles

Day 3: Implementing and Populating the Data Catalog

• Tools and platforms for catalog development

• Best practices for data onboarding and quality control

Day 4: Leveraging the Catalog for AI Project Success

• Facilitating data discovery and reuse

• Monitoring catalog usage and continuous improvement