Mar 15–18, 2020

Schedule: Machine Learning Model Governance and Operations sessions

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9:00am12:30pm Monday, March 16, 2020
Location: LL21 D
Danilo Sato (ThoughtWorks)
We will walk you through applying continuous delivery (CD), pioneered by ThoughtWorks, to data science and machine learning. Join in to learn how to make changes to your models while safely integrating and deploying them into production, using testing and automation techniques to release reliably at any time and with a high frequency. Read more.
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1:30pm5:00pm Monday, March 16, 2020
Location: LL21 C
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Machine learning models are data, which means they require the same data governance considerations as the rest of your data. In this tutorial we will concentrate on metadata management for model serving. We will discuss what information about running systems we need and why it is important. We will also show how Apache Atlas can be used for storing and managing this information. Read more.
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1:30pm5:00pm Monday, March 16, 2020
Location: LL21 E/F
Patrick Hall (H2O.ai | George Washington University)
Even if you've followed current best practices for model training and assessment, machine learning models can be hacked, socially discriminatory, or just plain wrong. This presentation introduces model debugging strategies to test and fix security vulnerabilities, unwanted social biases, and latent inaccuracies in models. Read more.
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11:00am11:40am Tuesday, March 17, 2020
Location: LL20C
Dean Wampler (Lightbend), Boris Lublinsky (Lightbend)
Production deployment of ML models requires Data Governance, because models are data. This session justifies that claim, then explores its implications and techniques for satisfying the requirements. Using motivating examples, we’ll explore reproducibility, security, traceability, and auditing, plus some unique characteristics of models in production settings. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL21 E/F
David Talby (Pacific AI)
As an industry, we have about forty years of experience forming best practices and tools for storing, versioning, collaborating, securing, testing, and building software source code – but only about four years doing so for AI models. This talk will catch you up on current best practices and freely available tools so that your team can go beyond experimentation to successfully deploy models. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL21 E/F
Alice Zheng (Amazon)
Four lessons in building and operating large-scale, production grade machine learning systems at Amazon, useful for practioners and would-be practioners in the field. Read more.

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