Build Better Machine Learning Models With Confidence By Adding Validation With Deepchecks

The Python Podcast.__init__
The podcast about Python and the people who make it great
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This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
Machine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications.
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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Introduction and Episode Overview
Guest Introductions: Shir Kharev and Philip Tanner
Journey into Machine Learning
DeepChex: Continuous Validation for Machine Learning
Target Audience and Use Cases
Testing Machine Learning Models
Addressing Bias in Machine Learning
Defining Correctness in Machine Learning
DeepChex Package Implementation
Assumptions and Learnings from Open Source Community
Adopting DeepChex in Workflow
CICD and Model Validation
Commercial Aspects and Governance
Interesting Use Cases and Lessons Learned
When DeepChex is Not the Right Choice
Future Plans for DeepChex
Biggest Barrier to Machine Learning Adoption
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