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)
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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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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11:50am12:30pm Tuesday, March 17, 2020
Location: LL21 E/F
Shubhankar Jain (SurveyMonkey), Aliaksandr Padvitselski (SurveyMonkey), Manohar Angani (SurveyMonkey)
Every organization is leveraging machine learning (ML) to provide increasing value to their customers and understand their business. You may have created models too. But, how do you scale this process now? In this case study, you will learn 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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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL20D
Zak Hassan (Red Hat Inc)
The number of logs are increasing constantly and no human will, or can, monitor them all. We employ NLP for text encoding and machine learning methods for automated anomaly detection, in an effort to construct a tool that could help developers perform root cause analysis more quickly on failing applications. Also, provide 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)
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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1:45pm2:25pm Wednesday, March 18, 2020
Location: LL21 E/F
Ananth Kalyan Chakravarthy Gundabattula (Commonwealth Bank of Australia)
Perhaps it is no exaggeration to state that feature engineering can make or break a machine learning model. Featuretools package and the associated algorithm are accelerating the way features are built.The talk covers 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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