AI Engineering Podcast

Validating Machine Learning Systems For Safety Critical Applications With Ketryx

Nov 8, 2023

51:12

Snipd AI

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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Podcast summary created with Snipd AI

Quick takeaways

Regulations in the medical field require robust validation and evidence to ensure software safety and compliance.

Machine learning has transformative potential in medical applications, improving diagnostics and reducing healthcare costs.

Collaboration and tools are essential for efficient and compliant software development in regulated industries.

Deep dives

The challenges of developing regulated software in the medical field

Developing regulated software in the medical field carries significant challenges, particularly in highly regulated industries. Regulations require robust validation and evidence that your system performs as intended. This includes demonstrating that the system does not pose harm to users and meets strict requirements. For highly regulated industries, such as medical applications, there are additional burdens of regulation and evidence. These burdens encompass product regulations, compliance with standards, and proper documentation of system functionality. Developing validated software in the medical field requires subject matter expertise, a sophisticated approach to risk management, and effective change management processes.

Introduction

2min

Background and Experience in Machine Learning

2min

Regulatory Burdens on ML Teams in Medical Applications

23min

Validating Machine Learning Systems for Safety-Critical Applications

17min

Excitement and Enthusiasm in the Medical Field for Improving Medical Applications

2min

Building Software in Medicine with Catrix

4min

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

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

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers.

Links

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

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AI Engineering Podcast

Validating Machine Learning Systems For Safety Critical Applications With Ketryx

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The challenges of developing regulated software in the medical field

The impact of machine learning in the medical field

The importance of regulatory compliance in software development

The role of collaboration and tools in software development for regulated industries

Overcoming barriers to machine learning adoption in the medical field

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podcast app

AI-powered
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Discover
highlights

Save any
moment

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AI Engineering Podcast

Validating Machine Learning Systems For Safety Critical Applications With Ketryx

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The challenges of developing regulated software in the medical field

The impact of machine learning in the medical field

The importance of regulatory compliance in software development

The role of collaboration and tools in software development for regulated industries

Overcoming barriers to machine learning adoption in the medical field

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AI-poweredpodcast player

Discoverhighlights

Save anymoment

Share& Export

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Discoverhighlights

Get the Snipd
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