Astronomer DataOps in the Age of AI
Location: Harris' Restaurant - The San Francisco Steakhouse
As AI initiatives grow in scale and complexity, traditional data management practices often fall short. This talk will highlight how a foundational DataOps strategy can streamline data workflows and accelerate AI projects. By unifying data pipelines, automating processes, and ensuring consistent data quality, organizations can move faster and more reliably in delivery of next-gen AI initiatives.
We’ll explore how DataOps enables stronger inference patterns for LLMs, guarantees quality for downstream outputs, and provides the scalability needed for AI at enterprise scale. Attendees will leave with actionable insights on how adopting a DataOps platform can drive their AI initiatives forward.
Key Discussion Topics:
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Uncovering Hidden Data Issues with Data Observability:
A deep dive into how organizations can use data observability to identify hidden data quality issues and pipeline bottlenecks, even those that might go unnoticed. Attendees will discuss proactive strategies for improving data reliability and the impact of this on delivering accurate, timely insights. -
Ensuring Resilient Data Pipelines with Data Orchestration:
An exploration of how data orchestration tools can help organizations adapt to changes in data sources, formats, and evolving business requirements. The session will focus on building resilient and flexible pipelines to support continuous, high-quality data flow. -
Optimizing Data Orchestration with Observability Insights:
Sharing real-world examples of how data observability insights can directly inform and enhance data orchestration processes. The discussion will include best practices for improving pipeline efficiency, reducing data quality issues, and streamlining workflows. -
The Role of Human Expertise in Data Management:
While automation plays a critical role in data observability and orchestration, human expertise remains essential. Participants will discuss how human intervention continues to shape successful data management, and how this dynamic might evolve with advances in technology.
By the end of this session, participants will have actionable insights on how to leverage data observability and orchestration to drive better data quality, improve operational efficiency, and ensure long-term data pipeline resilience.