Laying the Groundwork for GenAI Success with Data Observability, Lineage, and Quality
Date:
Tuesday, November 26 2024
Time: 1:00 PM (PST)
Location: Virtual
Duration: 1 HourÂ
Register Here
First Step is to Register so we can follow up with you for more details.
Laying the Groundwork for GenAI Success with Data Observability, Lineage, and Quality
Date:Â
Tuesday, November 26 2024
Time: 1:00 PM (PST)
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
Explore the critical role of data observability, lineage, and quality in achieving GenAI success. This comprehensive workshop will equip you with the knowledge and skills to build a resilient AI foundation through optimized data practices.
Key Topics
- Understand the Critical Role of Data Observability for GenAI Success
- Uncover the Importance of Data Lineage for Transparency and Traceability
- Discover How Data Quality Impacts AI Reliability and Fairness
- Integrate Observability, Lineage, and Quality to Build a Resilient AI Foundation
Common Use Cases
- Proactive Data Pipeline Monitoring for AI Models
- Enhancing Regulatory Compliance with Data Lineage
- Reducing AI Bias by Ensuring Data Quality
- Effective Troubleshooting with Integrated Observability and Lineage
- Using Data Maturity Assessments to Support AI Readiness
Workshop Agenda
Building the Foundation for GenAI Success
- Explore Data Observability, Lineage, and Quality as essential for GenAI
- Introduction to the Data Maturity Model for assessing AI readiness
- Real-world example highlighting the impact of data practices on AI
Data Observability: Ensuring Data Health for AI Models
- Importance of data observability for detecting anomalies, monitoring data flow, and maintaining data consistency
- Key practices for tracking data freshness, completeness, and accuracy across data sources
- Examples of how data observability enhances AI model stability and reliability
Integrating Observability, Lineage, and Quality for GenAI Readiness
- How these practices work together to support scalable and reliable AI
- Using the Data Maturity Model to assess and improve readiness
- Discussion on challenges and next steps for GenAI data readiness
Data Lineage: Tracing Data for Transparency and Accountability
- Significance of lineage for tracking data origins, transformations, and data flow
- Role in ensuring compliance, accountability, and efficient troubleshooting for GenAI models
- Illustration of how lineage documentation improves transparency and trust in AI outcomes
Data Quality: Setting Standards for Reliable AI Outcomes
- Overview of quality dimensions: completeness, accuracy, consistency, and timeliness
- Impact of quality on AI accuracy and minimizing bias
- Demo showing data quality improvements for better AI performance
Target Audience
Job Titles or Departments
- CIO, CTO, CDO, CISO, VP of Engineering, Enterprise Architect
- Director or Engineering Manager (Software)
- Director or Manager of Data Science/Analytics (or Sr Data Scientist)
- Director or Manager of Cloud Engineering (or Sr Cloud Engineer)
Suresh Venkatraman
A-VP Product Engineering, People Tech
November 26, 2024
1:00 PM (PST)
Virtual
Workshop Outcomes
Recognize the Role of Data in GenAI Success
- Understand how data observability, lineage, and quality directly impact AI reliability and transparency
- Gain clarity on why these practices are foundational to building and maintaining effective GenAI applications
Learn Practical Tools and Techniques for Data Optimization
- Discover actionable methods for enhancing observability, tracing data lineage, and ensuring data quality in AI initiatives
- Take away specific strategies to strengthen data systems, enabling more consistent and accurate AI results
Assess Organizational Readiness for GenAI
- Use insights from the Data Maturity Model to gauge your organization's data capabilities
- Identify key areas for improvement to support successful, scalable GenAI implementation
Integrate Core Data Practices for a Resilient AI Foundation
- See how observability, lineage, and quality work together to create a strong data foundation
- Understand how integrating these practices can maximize GenAI's potential, building resilience and scalability into AI initiatives
Join us to learn how data observability, lineage, and quality are essential for GenAI success. Gain practical insights, assess your organization’s readiness, and build a strong foundation for your AI initiatives!
