Name: From Black Box to Glass Box: Observability for Scalable AI Systems
Start: 2025-12-11T14:00:00-05:00
End: 2025-12-11T15:00:00-05:00

As AI systems continue to scale, observability has become an essential component for success. This webinar explores two key dimensions: data observability, which focuses on monitoring and improving the quality of data that drives AI systems, and AI observability, which provides transparency and accountability for model performance and decision-making.

Learn practical strategies for building "glass box" AI systems where insights are clear, issues are easy to diagnose, and scalability is seamless.

**Key Takeaways:**

* **Understand Data Observability:** Learn how to implement monitoring systems to ensure high-quality, reliable data pipelines for AI systems.
* **Explore AI Observability:** Discover tools and techniques for monitoring AI models, ensuring they are transparent, unbiased, and explainable.
* **Bridge the Gap:** Integrate data and AI observability practices to achieve robust, scalable, and trustworthy AI deployments.

Panelists to be announced soon

**[Register Here](https://hubs.ly/Q031Fp0z0)**

Amelia Mink

Feature Engineers NYC

datasciconnect

Technology

Data Analytics

Big Data

Open Source

Text Analytics

Predictive Modeling

Predictive Analytics

Laurie Hood

Rishi Takrani

Anna Nerezova

John Rabovich

Peter Shagnea

Arielle

Lindsay M

From Black Box to Glass Box: Observability for Scalable AI Systems

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Feature Engineers NYC

From Black Box to Glass Box: Observability for Scalable AI Systems

Feature Engineers NYC

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