Mar 15–18, 2020

Schedule: Machine Learning Model Development Lifecycle sessions

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9:00am12:30pm Monday, March 16, 2020
Location: LL21 D
Danilo Sato (ThoughtWorks)
Danilo Sato lead you through applying continuous delivery (CD) to data science and machine learning (ML). 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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11:00am11:40am Tuesday, March 17, 2020
Location: LL20C
Dean Wampler (Anyscale), Boris Lublinsky (Lightbend)
Production deployment of machine learning (ML) models requires data governance, because models are data. Dean Wampler and Boris Lublinsky justify that claim and explore its implications and techniques for satisfying the requirements. Using motivating examples, you'll explore reproducibility, security, traceability, and auditing, plus some unique characteristics of models in production settings. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: LL21 E/F
Shubhankar Jain (SurveyMonkey), Aliaksandr Padvitselski (SurveyMonkey), Manohar Angani (SurveyMonkey)
Every organization leverages ML to increase value to customers and understand their business. You may have created models, but now you need to scale. Shubhankar Jain, Aliaksandr Padvitselski, and Manohar Angani use a case study to teach you how to pinpoint inefficiencies in your ML data flow, how SurveyMonkey tackled this, and how to make your data more usable to accelerate ML model development. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: LL21 E/F
Kelley Rivoire (Stripe)
Tools for training and optimizing models have become more prevalent and easier to use; however, these are insufficient for deploying ML in critical production applications. Kelley Rivoire dissects how Stripe approached challenges in developing reliable, accurate, and performant ML applications that affect hundreds of thousands of businesses. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL20D
Zak Hassan (Red Hat)
The number of logs increases constantly and no human can monitor them all. Zak Hassan employs natural language processing (NLP) for text encoding and machine learning (ML) methods for automated anomaly detection to construct a tool to help developers perform root cause analysis more quickly. He provides a means to give feedback to the ML algorithm to learn from false positives. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL21 E/F
David Talby (Pacific AI)
The industry has about 40 years of experience forming best practices and tools for storing, versioning, collaborating, securing, testing, and building software source code—but only about 4 years doing so for AI models. David Talby catches you up on current best practices and freely available tools so your team can go beyond experimentation to successfully deploy models. Read more.
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1:45pm2:25pm Wednesday, March 18, 2020
Location: LL21 E/F
Ananth Kalyan Chakravarthy Gundabattula (Commonwealth Bank of Australia)
Feature engineering can make or break a machine learning model. The featuretools package and associated algorithm accelerate the way features are built. Ananth Kalyan Chakravarthy Gundabattula explains a Dask- and Prefect-based framework that addresses challenges and opportunities using this approach in terms of lineage, risk, ethics, and automated data pipelines for the enterprise. Read more.

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