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.Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!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 IntroductionHow 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/data scientists/domain experts can define what "correctness" means in a given model or subject area? In software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models?How do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks?Can you describe how the DeepChecks package is implemented? How have the design and goals of the project changed or evolved from when you started working on it?What are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners? Can you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle?Test engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices?What are the interfaces available for creating reusable tests and composing test suites together?What are ...
