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The Hard Part of Machine Learning with Lynn Langit

Jun 26, 2024

Expert Lynn Langit discusses the challenges of using machine learning to improve patient outcomes at Mayo Clinic. She highlights the complexities of multi-modal data analytics, testing machine learning models, and the importance of focusing on fundamentals before advanced goals. The conversation explores practical applications in bioinformatics, statistical tests for machine learning, and the impact of new technologies like Gemini in Google Cloud on productivity and collaboration.

35:08

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Episode guests

Lynn Langit

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Quick takeaways

Challenges of multi-modal data analytics include integrating diverse patient data for comprehensive treatment evaluation.

Practical machine learning success relies on mastering fundamentals before advancing to complex tasks.

Deep dives

Lynn Langit's Versatility Across Cloud Platforms

Lynn Langit, a noted cloud architect working with GCP, AWS, and Azure, is known for her technical expertise and customer-centric approach. Her recognition as a Google developer expert in cloud and AI, an AWS community hero for data, and a Microsoft regional director underscores her agnostic stance, prioritizing serving customers across diverse platforms for optimal solutions.

Intro

2min

Challenges and Advancements in Tech Industry Customer Work

15min

Challenges of Adopting New Machine Learning Models in Traditional Fields

5min

Developing Intuition in Machine Learning through Music and Images

2min

Exploring Programming Problems Through a Linguistics Lens

2min

Enhancing Productivity and Collaboration with GitHub Workspace and Gemini in Google Cloud

5min

Challenges and Adaptation in Machine Learning Ecosystem

5min

What are the hard parts of machine learning? Richard chats with Lynn Langit about her work helping the Mayo Clinic improve patient outcomes using machine learning to understand patient data better. Lynn talks about the challenges of multi-modal data analytics - taking all the different data collected from a patient, like an X-ray or video, along with treatment notes, to create an overall picture of treatment and outcome. Then multiply that by thousands of patients, making a complicated data problem with huge challenges in testing and validation. How do you know that the machine learning model is correct? The key to practical machine learning is in the fundamentals - working on each step before you jump to the more complex goals!

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Recorded May 17, 2024

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RunAs Radio

The Hard Part of Machine Learning with Lynn Langit

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Lynn Langit's Versatility Across Cloud Platforms

Venturing into the Cloud with Mayo Clinic and the Broad Institute

Challenges in Implementing Machine Learning Models at Mayo Clinic

The Significance of Multimodal Data in Healthcare Analysis

Remember Everything You Learn from Podcasts