Product Webinar: AI Observability for ML and Generative AI

May 23, 2023
10 AM PT / 1 PM ET
Registration is now closed. Please check back later for the recording.

We're thrilled to announce major product upgrades and support for LLMs. 

Watch this demo-driven webinar to learn how to:

  • Pinpoint model issues with contextual alerts and root cause analysis
  • Build flexible dashboards and reports to gain impactful insights
  • Monitor OpenAI embeddings and detect ‌drift
  • Evaluate the robustness of LLMs and NLP models
Featured Speakers
Sree Kamireddy
VP of Product
Fiddler AI
Sree leads the Product team at Fiddler. Sree brings a wealth of experience in scaling machine learning infrastructure and applying AI to tackle complex business challenges across a range of domains including Search, Ads, IoT, and Content Management.
Karen He
Principal Product Marketing Manager
Fiddler AI
Amal Iyer
Senior Staff AI Scientist
Fiddler AI
Amalendu (Amal) Iyer is a Sr. Staff Data Scientist at Fiddler where he is responsible for developing systems and algorithms to monitor, evaluate and explain ML models. He also leads the development of Fiddler Auditor, an open-source project to evaluate robustness and safety of Large Language models. Prior to joining Fiddler, Amal worked at HP Labs, where he led the research around Self-Supervised Learning techniques for improving data-efficiency of ML models and on Deep Reinforcement Learning. Before that at Qualcomm AI research, he was part of the team that developed the Snapdragon Neural Processing SDK and developed Speech Recognition models for Voice UI applications. Amal obtained his M.S. from University of Florida and B.S. from University of Mumbai in Electrical and Computer Engineering.
Bashir Rastegarpanah
Data Scientist
Fiddler AI
Bashir is a data scientist at Fiddler who has worked on designing and prototyping various features for Fiddler’s monitoring platform such as Fiddler Vector Monitoring. Prior to Fiddler, he received his PhD at Boston University where he worked on different aspects of responsible AI including privacy and explainability.