Mar 15–18, 2020

Schedule: Deep dive into specific tools, platforms, or frameworks sessions

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9:00am - 5:00pm Sunday, March 15.. & Sunday, March 15
Location: 211 D
Don Fox (Pragmatic Institute)
We will walk through all the steps - from prototyping to production - of developing a machine learning pipeline. We’ll look at data cleaning, feature engineering, model building/evaluation, and deployment. Students will extend these models into two applications from real-world datasets. All work will be done in Python. Read more.
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9:00am12:30pm Monday, March 16, 2020
Location: LL21B
Secondary topics:  Streaming and IoT
David Anderson (Ververica), Seth Wiesman (Ververica)
This tutorial demonstrates that building and managing scalable, stateful, event driven applications can be easier and more straightforward than you might expect. We’ll provide a hands-on introduction to this topic as we implement a ridesharing application together. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: LL21 C
Secondary topics:  Streaming and IoT
Minal Mishra (Netflix)
In this talk we will spend time to describe the Netflix's video player release process, the challenges with deriving time series metrics from a firehose of events and discuss some of the oddities in running analysis on realtime metrics. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL21 E/F
Secondary topics:  Cloud Platforms and SaaS
Rustem Feyzkhanov (Instrumental)
Machine and deep learning become more and more essential for a lot of businesses for internal and external use. One of the main issues with deployment is finding the right way to train and operationalize model within the company. Serverless approach for deep learning provides cheap, simple, scalable and reliable architecture for it. My presentation will show how to do so within AWS infrastructure. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL20D
Liqun Shao (Microsoft)
We outline material from the newly released GitHub repository to show how data scientists without NLP knowledge can quickly train, evaluate, and deploy state-of-the-art NLP models. We will demonstrate and focus on two use cases with distributed training on Azure Machine Learning with Horovod: GenSen model for sentence similarity and BERT for question-answering using Jupyter notebooks for Python. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL21B
Secondary topics:  Streaming and IoT
Jeff Chao (Netflix)
At Netflix, we've experienced an unprecedented global increase in membership over the last several years. Production outages today have far greater impact in much less time than it did compared to years before. In order to continue providing great experiences for our members, we've built and open sourced Mantis which enables us to get realtime, granular, cost-effective operational insights. Read more.
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1:45pm2:25pm Wednesday, March 18, 2020
Location: LL21B
Secondary topics:  Data Management and Storage
Qorry Asfar (Pusat Demokrasi dan Hak Asasi Manusia), Muhammad Asfar (University of Airlangga)
With the disclose of Cambridge Analytical scandal, political practitioners has started to adopt big data technology to give better understanding and management of the data. This presentation aims to provide big data case study to develop political strategy and how technological adoption will shape better political landscape in the future. 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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