Become AI-ready by Securing your GenAI with Data Governance
Date:
Tuesday, November 26 2024
Time: 2:00 PM (EST)
Location: Virtual
Duration: 1 hour
Register Here
First Step is to Register so we can follow up with you for more details.
Become AI-ready by Securing your GenAI with Data Governance
Date:
Tuesday, November 26 2024
Time: 2:00 PM (EST)
Location: Virtual
Duration: 1 hour
Register Here
First Step is to Register so we can follow up with you for more details.
About the workshop
Join us for an immersive workshop designed to elevate your understanding and implementation of data governance in the era of generative AI. This comprehensive session will cover the essentials of data and ML governance, effective strategies, and the integration of advanced technologies like AWS and Azure to ensure robust data management.
Agenda
Data Governance – Ensuring Data is Fit for Purpose
- Data Quality Management: Implement processes and tools to ensure data accuracy, completeness, consistency, and reliability.
- Data Stewardship: Assign roles and responsibilities for managing data assets, including data owners and data stewards.
- Data Lineage: Track the origins, movements, and transformations of data throughout its lifecycle to ensure transparency and trustworthiness.
ML Governance – Managing and Enabling the ML Process
- Model Versioning and Tracking: Implement tools and practices to version control ML models and track their changes over time.
- Bias and Fairness: Ensure models are trained and evaluated with fairness in mind, minimizing bias in the ML process.
- Compliance and Auditing: Establish mechanisms to audit ML processes and ensure compliance with regulatory requirements.
Data Governance Strategy
- Policy Development: Create and enforce data governance policies that align with organizational goals and regulatory requirements.
- Stakeholder Engagement: Involve key stakeholders across the organization to ensure buy-in and collaboration in data governance efforts.
- Metrics and KPIs: Define and monitor key performance indicators to measure the effectiveness of data governance initiatives.
Governance with Generative AI Patterns
- Ethical AI Frameworks: Implement guidelines and practices to ensure the ethical use of generative AI technologies.
- Intellectual Property Management: Protect and manage intellectual property generated by AI systems, including copyright and patent considerations.
- Risk Mitigation: Identify and mitigate risks associated with generative AI, such as data privacy concerns and potential misuse.
Data Governance in the End-User Critical Path
- User Access Controls: Establish strict access controls to ensure only authorized users can access sensitive data.
- Data Usage Policies: Define clear policies on how end-users can utilize data, ensuring compliance and security.
- End-User Training: Provide training and resources to end-users to promote understanding and adherence to data governance practices.
Data Governance Behind the Scenes
- Metadata Management: Implement systems to manage metadata, ensuring data is well-documented and easily discoverable.
- Data Integration: Ensure seamless integration of data from various sources, maintaining consistency and accuracy.
- Data Storage and Archiving: Establish policies for data storage, retention, and archiving to meet regulatory and organizational requirements.
AWS Comprehensive Data Governance Foundation
- AWS Lake Formation: Utilize AWS Lake Formation to build secure data lakes with robust governance features.
- AWS Glue: Leverage AWS Glue for data cataloging and ETL (Extract, Transform, Load) operations, ensuring data quality and consistency.
- IAM and Security Policies: Implement AWS Identity and Access Management (IAM) and security policies to protect data assets and ensure compliance.
Azure Comprehensive Data Governance Foundation
- Azure Purview: Use Azure Purview for data discovery, classification, and lineage tracking across the Azure ecosystem.
- Azure Data Catalog: Implement Azure Data Catalog to enable a centralized repository for metadata management and data governance.
- Azure Policy: Utilize Azure Policy to enforce organizational standards and compliance requirements across data assets.
Comprehensive Data Governance Operating Model
- Governance Framework: Establish a comprehensive framework that outlines roles, responsibilities, policies, and procedures for data governance.
- Cross-Functional Collaboration: Encourage collaboration between IT, legal, compliance, and business units to ensure holistic data governance.
- Continuous Improvement: Implement a feedback loop to continuously assess and improve data governance practices, ensuring they remain effective and relevant.
Takeaways
By the end of this workshop, participants will:
- Comprehensive Understanding of Data and ML Governance: Participants will learn the foundational principles and best practices for managing data and machine learning (ML) processes, including data quality management, stewardship, lineage tracking, and ethical considerations.
- Practical Implementation Strategies: Attendees will gain practical knowledge on implementing data governance frameworks and tools, such as AWS Lake Formation, Azure Purview, and metadata management systems, to ensure data is accurate, secure, and accessible.
- Develop Effective Incident Response Strategies: Learn robust incident response strategies for quicker and more efficient resolution of security incidents.
- Techniques for Managing Bias and Ensuring Fairness The workshop will cover methods to identify, mitigate, and monitor biases in ML models, ensuring fairness and compliance with ethical standards.
- Policy Development and Regulatory Compliance: Participants will learn how to develop, enforce, and update data governance policies that align with organizational goals and regulatory requirements, as well as how to conduct audits and maintain compliance.
- Enhanced Stakeholder Engagement and Cross-Functional Collaboration: The session will emphasize the importance of involving key stakeholders, fostering collaboration across business, IT, legal, and compliance units, and establishing a governance framework that supports continuous improvement and adaptability.
Ideal For
- CIO, CTO, CPO, CDO, CISO, VP of Engineering, Enterprise Architect
- Director or Engineering Manager (Software)
- Director or Manager of DevOps Engineering (or Sr Data Engineer)
- Director or Manager of Data Science/Analytics (or Sr Data Scientist)
