Join us for a live webinar!
Enhancing AI Testing : The Power of Metadata Hydration
June 12, 2024
9:00 am PT / 12:00 pm ET
Join us for an engaging and enlightening webinar focused on the effective utilization of metadata in machine learning and advanced techniques for metadata hydration. This session is designed for ML industry practitioners who are keen on elevating the quality and efficiency of their models.
We will cover:
-The benefits of having a variety of metadata
-How metadata makes for potent testing
-The utility of automatically extracted metadata
-Plus a brief demo of how Kolena automatically extracts relevant metadata for various datasets
This webinar is an essential opportunity for ML professionals looking to leverage metadata effectively to refine model performance and ensure their projects not only meet but exceed industry standards. Join us to transform how you develop, test, and deploy machine learning models with precision and confidence.
Save your seat now!
We will cover:
-The benefits of having a variety of metadata
-How metadata makes for potent testing
-The utility of automatically extracted metadata
-Plus a brief demo of how Kolena automatically extracts relevant metadata for various datasets
This webinar is an essential opportunity for ML professionals looking to leverage metadata effectively to refine model performance and ensure their projects not only meet but exceed industry standards. Join us to transform how you develop, test, and deploy machine learning models with precision and confidence.
Save your seat now!
This webinar has already happened, but you can watch it HERE!
Meet The Speakers
Join our expert speakers for a presentation and Q&A
Saad Hossain
Saad is a machine learning engineer at Kolena on our research team. His motivation is to use technology in order to better our surroundings and is always excited to learn something new.
Skip Everling
At Kolena, Skip's role as Head of Developer Relations. His objective is to help ML/AI engineers and data scientists more effectively test and refine their models so that they perform robustly in the real world.