GenAI performance is constrained by the data pipelines that feed it, yet many teams lack disciplined data operations tailored to GenAI workloads. Without strong data ops practices, drift, governance gaps, and unusable feedback loops quickly erode system quality.
To win, your GenAI solutions must be powered by well-defined pipelines, governed data, and continuous data quality monitoring.
When GenAI data operations are immature, teams struggle to sustain model quality and trust:
- Ad hoc data pipelines: GenAI inputs are sourced and processed inconsistently across environments and use cases.
- Unreliable data quality: Poor cleaning, normalization, and transformation introduce noise and hidden bias.
- Weak governance and lineage: Teams lose visibility into where data comes from, how it changes, and whether it can be trusted.
These issues drive model drift, compliance risk, and declining GenAI performance over time.
In this hands-on workshop, your team applies GenAI-specific data operations best practices through guided exercises and real-world scenarios.
- Define robust data pipelines tailored to GenAI ingestion and processing needs.
- Apply cleaning, normalization, and transformation techniques to stabilize GenAI inputs.
- Design labeling and feedback integration workflows that continuously improve data quality.
- Establish data lineage and governance practices aligned to GenAI requirements.
- Monitor data quality to detect drift, gaps, and degradation early.
- Defining Data Pipelines for GenAI
- Cleaning, Normalizing, and Transforming Inputs
- Labeling and Feedback Integration Workflows
- Maintaining Data Lineage and Governance
- Monitoring Data Quality for Drift and Gaps
- Design GenAI data pipelines that are consistent and repeatable across environments.
- Improve input quality through structured cleaning and transformation practices.
- Integrate labeling and feedback workflows into ongoing data operations.
- Maintain clear lineage and governance over GenAI data assets.
- Detect and respond to data drift before it impacts model behavior.
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Data processing frameworks, labeling tools, and monitoring solutions in a guided environment