Presentation: Interpretable Machine Learning Products
Abstract
Interpretable models are easier to improve. Regulators and society can better trust them to be safe and nondiscriminatory. They can also offer insights that can be used to change real-world outcomes for the better. But because there is a central tension between accuracy and interpretability interpretability can be hard to ensure.
I'll explore both the product case for interpretability and the academic research that is starting to make the inner workings of black box models such as deep neural networks easier to understand. In particular, I'll look at the application of a new open source tool called LIME to customer churn, image classification and black box NLP models.
2019 Tracks
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Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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Papers in Production: Modern CS in the Real World
Groundbreaking papers make real-world impact.
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Hands-on Codelabs & Speakers Office Hours
Codelabs are a self-guided tutorial of a product, API, or tool kit followed by an Office Hour period with the lab’s creator.
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Solving Software Engineering Problems with Machine Learning
Interesting machine learning use cases changing how we develop software today, including planned topics touching on infrastructure optimization, developer experience, security, and more.
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Predictive Architectures in the Real World
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.
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Predictive Data Pipelines & Architectures
Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix
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Sequential Data: Natural Language, Time Series, and Sound
Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.
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ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
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Deep Learning in Practice
Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.