Name
401: LabelOps: The Rocket Fuel for Image Analytics
Date & Time
Wednesday, October 16, 2024, 3:15 PM - 4:00 PM
Focus Area
Analytics Architecture & Technology, Data Science
Description

Running a successful image analytics program involves constant monitoring and frequent adjustments. Drone, LiDAR, and satellites produce volumes of images but, without proper labeling procedures in place, how can your team ensure they’re leveraging the full power of your investments? Labeling Operations (LabelOps) stand at the forefront of ensuring data integrity and precision for utilities to make data-informed decisions.

This session will uncover the critical role of LabelOps in building and sustaining high-performing AI models for your analytics team. Discover how the incorporation of advanced techniques, including image augmentation and synthetic data, can improve the image labeling process.

By focusing on LabelOps, a critical but sometimes neglected element, this session will offer practical and actionable insights for attendees looking to significantly enhance their asset image analytics implementations and reveal the true potential of their team’s visual assets.

Session Highlights:

  • Identify the components of a scalable labeling process: Discover best practices for creating high-quality labeled data, the foundation for accurate AI models.
  • Optimize LabelOps for cross-functional integration: Learn how to effectively align your LabelOps strategy with broader organizational goals, facilitating collaboration and setting governance across different teams while enhancing the overall impact of AI initiatives.
  • Detect and prepare for implementation challenges: Between low-quality data labels, scaling issues, and inefficient processes, computer vision initiatives face issues that can stall your team’s initiatives. Learn how your organization can prepare for and prevent potential interruptions, leading to quicker deployments of AI-powered solutions.
  • Boost ROI with consistent labeling strategies and strategic planning: Understand how blind-quality control and a well-defined taxonomy ensure consistent labeling, resulting in a more robust model performance.
Alexander Johnson
Sponsored by: