The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
Schedule: Extensions and customization sessions
9:00am - 5:00pm Tuesday, August 21 & Wednesday, August 22
2-Day Training
Location: Concourse B
Level: Intermediate
Zachary Glassman (The Data Incubator)
Average rating:
(4.50, 2 ratings)
Zachary Glassman leads a hands-on dive into building intelligent business applications using machine learning, walking you through all the steps of developing a machine learning pipeline. You'll explore data cleaning, feature engineering, model building and evaluation, and deployment and extend these models into two applications using real-world datasets.
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11:05am–11:45am Thursday, August 23, 2018
Location: Murray Hill
Level: Beginner
Diogo Castro (CERN)
SWAN, CERN’s service for web-based analysis, leverages the power of Jupyter to provide the high energy physics community access to state-of-the-art infrastructure and services through a web service. Diogo Castro offers an overview of SWAN and explains how researchers and students are using it in their work.
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11:55am–12:35pm Thursday, August 23, 2018
Location: Murray Hill
Level: Beginner
Adam Thornton (LSST)
LSST is an ambitious project to map the sky in the fastest, widest, and deepest survey ever made. The project's database disrupts traditional astronomical workflows, and its science platform requires a paradigm shift in how astronomy is done. Adam Thornton discusses the challenges of providing production services on a notebook-based architecture and the compelling advantages of JupyterLab.
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11:55am–12:35pm Thursday, August 23, 2018
Location: Nassau
Level: Beginner
Chris Harris (Kitware)
Average rating:
(5.00, 1 rating)
In silico prediction of chemical properties has seen vast improvements in both veracity and volume of data but is currently hamstrung by a lack of transparent, reproducible workflows coupled with environments for visualization and analysis. Chris Harris offers an overview of a platform that uses Jupyter notebooks to enable an end-to-end workflow from simulation setup to visualizing the results.
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1:50pm–2:30pm Thursday, August 23, 2018
Location: Sutton Center/Sutton South
Level: Intermediate
Matthew Seal (Netflix)
Using an nteract project, papermill, Matthew Seal walks you through how Netflix uses notebooks to track user jobs and make a simple interface for work submission. You’ll get an inside peek at how Netflix is tackling the scheduling problem for a range of users who want easily managed workflows.
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1:50pm–2:30pm Thursday, August 23, 2018
Location: Nassau
Level: Intermediate
Average rating:
(2.00, 3 ratings)
Notebook authors often consider only the interactive experience of creating computable documents. However, the dynamic state of a notebook is a minor period in its lifecycle; the majority is spent as a file at rest. Tony Fast and Nick Bollweg explore conventions that create notebooks with value long past their inception as documents, software packages, test suites, and interactive applications.
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4:10pm–4:50pm Thursday, August 23, 2018
Location: Sutton Center/Sutton South
Level: Intermediate
Average rating:
(5.00, 3 ratings)
Stephanie Stattel and Paul Ivanov walk you through a series of extensions that demonstrate the power and flexibility of JupyterLab’s architecture, from targeted functionality modifications to more extreme atmospheric changes that require extensive decoupling and flexibility within JupyterLab.
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5:00pm–5:40pm Thursday, August 23, 2018
Location: Sutton Center/Sutton South
Level: Intermediate
M Pacer (Netflix)
Average rating:
(4.50, 4 ratings)
Jupyter displays a rich array of media types out of the box. M Pacer explains how to use these capabilities to their full potential, covering how to add rich displays to existing and new Python classes and how to customize the way notebooks are converted to other formats. These skills will enable anyone to make beautiful objects with Jupyter.
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5:00pm–5:40pm Thursday, August 23, 2018
Location: Murray Hill
Level: Beginner
Bo Peng (The University of Texas, MD Anderson Cancer Center)
Bo Peng offers an overview of Script of Scripts (SoS), a Python 3-based workflow engine with a Jupyter frontend that allows the use of multiple kernels in one notebook. This unique combination enables users to analyze data using multiple scripting languages in one notebook and, if needed, convert scripts to workflows in situ to analyze large amounts of data on remote systems.
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11:05am–11:45am Friday, August 24, 2018
Location: Sutton Center/Sutton South
Level: Intermediate
Average rating:
(5.00, 3 ratings)
Reproducible data is essential for notebooks that work across time, across contributors, and across machines. Jackson Brown and Aneesh Karve demonstrate how to use an open source data registry to create reproducible data dependencies for Jupyter and share a case study in open science over terabyte-size image datasets.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Beginner
Douglas Blank (Comet.ML)
For the last four years, Douglas Blank has used nothing but Jupyter in the classroom—from a first-year writing course to a course on assembly language, from biology to computer science, from lectures to homework. Join in to learn how Douglas has leveraged Jupyter and discover the successes and failures he experienced along the way. Nicole Petrozzo then offers a student's perspective.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Nassau
Level: Intermediate
David Koop (University of Massachusetts Dartmouth)
Average rating:
(4.50, 2 ratings)
Dataflow notebooks build on the Jupyter Notebook environment by adding constructs to make dependencies between cells explicit and clear. David Koop offers an overview of the Dataflow kernel, shows how it can be used to robustly link cells as a notebook is developed, and demonstrates how that notebook can be reused and extended without impacting its reproducibility.
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1:50pm–2:30pm Friday, August 24, 2018
Location: Concourse A: Business Summit
Level: Beginner
Average rating:
(4.50, 2 ratings)
The key to successful threat detection in cybersecurity is fast response. George Williams, Harini Kannan, and Alex Comerford offer an overview of specialized extensions they have built for data scientists working in cybersecurity that can be used and deployed via JupyterHub.
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2:40pm–3:20pm Friday, August 24, 2018
Location: Murray Hill
Level: Intermediate
Randy Zwitch (MapD)
MapD Core is an open source analytical SQL engine that has been designed from the ground up to harness the parallelism inherent in GPUs. This enables queries on billions of rows of data in milliseconds. Randy Zwitch offers an overview of the MapD kernel extension for the Jupyter Notebook and explains how to use it in a typical machine learning workflow.
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2:40pm–3:20pm Friday, August 24, 2018
Location: Nassau
Level: Intermediate
Kevin Zielnicki (Stitch Fix)
Average rating:
(4.50, 2 ratings)
Even with good intentions, analysis notebooks can quickly accumulate a mess of false starts and out-of-order statements. Best practices encourage cleaning up a notebook to ensure reproducibility, but many analyses will never reach this cleaned-up state. Kevin Zielnicki offers an overview of Nodebook, a Jupyter plugin that encourages reproducibility by preventing inconsistency.
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4:10pm–4:50pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Intermediate
The nbinteract package converts Jupyter notebooks with widgets into interactive, standalone HTML pages. Its built-in support for function-driven plotting makes authoring interactive pages simpler by allowing users to focus on data, not callbacks. Sam Lau and Caleb Siu offer an overview of nbinteract and walk you through the steps to publish an interactive web page from a Jupyter notebook.
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4:10pm–4:50pm Friday, August 24, 2018
Location: Concourse A: Business Summit
Level: Intermediate
Average rating:
(5.00, 1 rating)
Hundreds of PayPal's data scientists, analysts, and developers use Jupyter to access data spread across filesystem, relational, document, and key-value stores, enabling complex analytics and an easy way to build, train, and deploy machine learning models. Romit Mehta and Praveen Kanamarlapudi explain how PayPal built its Jupyter infrastructure and powerful extensions.
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5:00pm–5:40pm Friday, August 24, 2018
Location: Beekman/Sutton North
Level: Beginner
Damián Avila (Anaconda, Inc.)
RISE has evolved into the main slideshow machinery for live presentations within the Jupyter notebook. Damián Avila explains how to install and use RISE. You'll also discover how to customize it and see some of its new capabilities. Damián concludes by discussing the migration from RISE into a new JupyterLab-RISE extension providing RISE-based capabilities in the new JupyterLab interface.
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