Validating Machine Learning Systems For Safety Critical Applications With Ketryx

Nov 8, 2023

Erez Kaminski, an expert in validating machine learning systems for safety critical applications, discusses the regulatory burdens on ML teams in medical applications, the challenges of validating ML systems, and opportunities for automating overhead. He also shares insights into the excitement in the medical field for improving medical applications and highlights the benefits of using Ketryx for building medical software.

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Episode notes

Introduction

00:00 • 2min

Background and Experience in Machine Learning

01:34 • 2min

Regulatory Burdens on ML Teams in Medical Applications

03:12 • 23min

Validating Machine Learning Systems for Safety-Critical Applications

25:58 • 17min

Excitement and Enthusiasm in the Medical Field for Improving Medical Applications

42:48 • 2min

Building Software in Medicine with Catrix

44:45 • 4min

Barriers to Adoption for Machine Learning and the Challenges in Machine Learning and Medicine

48:46 • 2min

Summary
Software systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work that he is doing at Ketryx to make that validation easier to implement and incorporate into the ongoing maintenance of software and machine learning products.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Erez Kaminski about using machine learning in safety critical and highly regulated medical applications

Interview

Introduction
How did you get involved in machine learning?
Can you start by describing some of the regulatory burdens placed on ML teams who are building solutions for medical applications?
- How do these requirements impact the development and validation processes of model design and development?
What are some examples of the procedural and record-keeping aspects of the machine learning workflow that are required for FDA compliance?
- What are the opportunities for automating pieces of that overhead?
Can you describe what you are doing at Ketryx to streamline the development/training/deployment of ML/AI applications for medical use cases?
- What are the ideas/assumptions that you had at the start of Ketryx that have been challenged/updated as you work with customers?
What are the most interesting, innovative, or unexpected ways that you have seen ML used in medical applications?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ketryx?
When is Ketryx the wrong choice?
What do you have planned for the future of Ketryx?

Contact Info

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0