Developing an AI/ML Data Strategy: Data Management & Governance in AI

Data analytics    |    Intermediate
  • 14 videos | 1h 1m 42s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
The rapidly growing fields of data analytics and artificial intelligence (AI) offer immense advantages to individuals and society. Nevertheless, there are also challenges related to data management and governance within the context of AI. Begin this course by exploring the practical knowledge and skills necessary for effective data management and governance in the context of AI projects. Discover how data quality, integrity, availability, and adherence to governance frameworks are crucial in AI projects. Next, examine data lineage, data privacy regulations, and data accessibility. Then focus on the risks of incomplete or biased data and methods for handling large and complex datasets. Finally, investigate metadata management, managerial responsibilities in data governance, and ethical considerations in data usage. At course completion, you will be able to effectively manage data in AI projects and navigate the complex landscape of AI and data analytics with a strong foundation in data management and governance principles.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Define the principles of effective data management and its relevance in artificial intelligence (ai) projects
    Describe the role of data quality, integrity, and availability in ai model development
    Outline the importance of data governance frameworks and their components
    Analyze strategies for data collection, storage, and integration in ai projects
    Outline the concept of data lineage and its significance in ai solutions
    Illustrate the role of data privacy regulations and compliance in ai initiatives
  • Provide an overview of strategies for managing data silos and ensuring data accessibility
    Outline the potential risks of biased or incomplete data in ai models
    Describe the methods for handling large and complex datasets in ai projects
    Analyze the role of metadata management in enhancing data quality
    Provide an overview of the managerial responsibilities in overseeing data governance committees
    Formulate guidelines for managing ethical considerations in data usage for ai
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 58s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 4m 58s
    After completing this video, you will be able to define the principles of effective data management and its relevance in artificial intelligence (AI) projects. FREE ACCESS
  • Locked
    3.  Data Quality, Integrity, and Availability
    6m 27s
    Upon completion of this video, you will be able to describe the role of data quality, integrity, and availability in AI model development. FREE ACCESS
  • Locked
    4.  Data Governance Frameworks
    3m 42s
    After completing this video, you will be able to outline the importance of data governance frameworks and their components. FREE ACCESS
  • Locked
    5.  Strategies for Data Collection, Storage, and Integration
    5m 34s
    Upon completion of this video, you will be able to analyze strategies for data collection, storage, and integration in AI projects. FREE ACCESS
  • Locked
    6.  Data Lineage
    3m 21s
    After completing this video, you will be able to outline the concept of data lineage and its significance in AI solutions. FREE ACCESS
  • Locked
    7.  Data Privacy Regulations and Compliance
    6m 23s
    Upon completion of this video, you will be able to illustrate the role of data privacy regulations and compliance in AI initiatives. FREE ACCESS
  • Locked
    8.  Data Accessibility
    4m 31s
    After completing this video, you will be able to provide an overview of strategies for managing data silos and ensuring data accessibility. FREE ACCESS
  • Locked
    9.  Biased and Incomplete Data in AI Models
    5m 47s
    Upon completion of this video, you will be able to outline the potential risks of biased or incomplete data in AI models. FREE ACCESS
  • Locked
    10.  Methods for Handling Large and Complex Datasets
    3m 5s
    After completing this video, you will be able to describe the methods for handling large and complex datasets in AI projects. FREE ACCESS
  • Locked
    11.  Metadata Management
    6m 19s
    Upon completion of this video, you will be able to analyze the role of metadata management in enhancing data quality. FREE ACCESS
  • Locked
    12.  Managerial Responsibilities in Overseeing Data Governance
    6m 8s
    After completing this video, you will be able to provide an overview of the managerial responsibilities in overseeing data governance committees. FREE ACCESS
  • Locked
    13.  Ethical Considerations in Data Usage
    3m 50s
    Upon completion of this video, you will be able to formulate guidelines for managing ethical considerations in data usage for AI. FREE ACCESS
  • Locked
    14.  Course Summary
    39s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Leveraging AI as a Team Member
Rating 4.7 of 21 users Rating 4.7 of 21 users (21)
Course Developing an AI/ML Data Strategy: Data Analytics & Data Ethics
Rating 4.6 of 155 users Rating 4.6 of 155 users (155)
Course Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
Rating 4.6 of 260 users Rating 4.6 of 260 users (260)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
Rating 4.6 of 260 users Rating 4.6 of 260 users (260)
Course Developing an AI/ML Data Strategy: Aspects of a Robust AI Strategy
Rating 5.0 of 2 users Rating 5.0 of 2 users (2)
Course Visualizing Data for Impact: Visual Design Theory
Rating 4.4 of 68 users Rating 4.4 of 68 users (68)