Preamble
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.
Summary
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.
Announcements
- Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
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- Your host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort.
Interview
- Introduction
- How did you get involved in machine learning?
- Can you describe what Deepchecks is and the story behind it?
- Who is the target audience for the project?
- What are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems?
- In the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn?
- What are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks?
- What are some examples of the kinds of tests that are useful for understanding the "correctness" of models?
- What are the methods by which ML engineers/da
