Brought to you by NumFOCUS Foundation and O’Reilly Media
The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
 
Concourse A
Add JupyterLab training  to your personal schedule
9:00am JupyterLab training Chris Colbert (Project Jupyter), Ian Rose (UC Berkeley), Saul Shanabrook (Quansight)
Concourse B
Add  Hands-on data science with Python (Day 2) to your personal schedule
9:00am Hands-on data science with Python (Day 2) Zachary Glassman (The Data Incubator)
Concourse E
Add Explore the AWS machine learning platform using Amazon SageMaker (Day 2) to your personal schedule
9:00am Explore the AWS machine learning platform using Amazon SageMaker (Day 2) Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Concourse F
Add Machine learning at scale with Kubernetes to your personal schedule
9:00am Machine learning at scale with Kubernetes Christopher Cho (Google)
Murray Hill A
Add Deploying a cloud-based JupyterHub for students and researchers to your personal schedule
9:00am Deploying a cloud-based JupyterHub for students and researchers Carol Willing (Cal Poly San Luis Obispo), Min Ragan-Kelley (Simula Research Laboratory), Erik Sundell (IT-Gymnasiet Uppsala)
Add How to build on top of Jupyter’s protocols to your personal schedule
1:30pm How to build on top of Jupyter’s protocols Kyle Kelley (Netflix)
Murray Hill B
Add An introduction to Julia in Jupyter to your personal schedule
9:00am An introduction to Julia in Jupyter Jane Herriman (Julia Computing)
Add Advanced Data Science to your personal schedule
1:30pm Advanced Data Science Bruno Gonçalves (JPMorgan Chase & Co.), Matt Brems (General Assembly)
Gramercy A
Add Network and graph analysis with Jupyter notebooks to your personal schedule
9:00am Network and graph analysis with Jupyter notebooks Noemi Derzsy (AT&T Labs)
Gramercy B
Add JupyterLab tutorial to your personal schedule
1:30pm JupyterLab tutorial Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS)
12:30pm Lunch | Room: Americas Hall 1
Add Jupyter Poster Session to your personal schedule
5:00pm Jupyter Poster Session | Room: Americas Hall 1
Add Jupyter Dine-Around to your personal schedule
7:00pm Jupyter Dine-Around | Room: Various Locations
8:00am Morning Coffee | Room: South Corridor
9:00am-5:00pm (8h) Training
JupyterLab training
Chris Colbert (Project Jupyter), Ian Rose (UC Berkeley), Saul Shanabrook (Quansight)
Chris Colbert, Ian Rose, and Saul Shanabrook walk you through using, extending, and developing custom components for JupyterLab using PhosphorJS, React, JavaScript, TypeScript, and CSS. You'll learn how to make full use of the power features of JupyterLab, customize it to your needs, and develop custom extensions, making complete use of JupyterLab's current capabilities.
9:00am-5:00pm (8h)
Hands-on data science with Python (Day 2)
Zachary Glassman (The Data Incubator)
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.
9:00am-5:00pm (8h)
Explore the AWS machine learning platform using Amazon SageMaker (Day 2)
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Machine learning and IoT projects are increasingly common at enterprises and startups alike and have been the key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev lead a hands-on deep dive into the AWS machine learning platform, using Project Jupyter-based Amazon SageMaker to build, train, and deploy ML/DL models to the cloud and AWS DeepLens.
9:00am-5:00pm (8h) Training
Machine learning at scale with Kubernetes
Christopher Cho (Google)
Christopher Cho demonstrates how Kubernetes can be easily leveraged to build a complete deep learning pipeline, including data ingestion and aggregation, preprocessing, ML training, and serving with the mighty Kubernetes APIs.
9:00am-12:30pm (3h 30m) JupyterHub deployments, Reproducible research and open science, Training and education
Deploying a cloud-based JupyterHub for students and researchers
Carol Willing (Cal Poly San Luis Obispo), Min Ragan-Kelley (Simula Research Laboratory), Erik Sundell (IT-Gymnasiet Uppsala)
Carol Willing, Min Ragan-Kelley, and Erik Sundell demonstrate how to provide easy access to Jupyter notebooks and JupyterLab without requiring users to install anything on their computers. You'll learn how to configure and deploy a cloud-based JupyterHub using Kubernetes and how to customize and extend it for your needs.
1:30pm-5:00pm (3h 30m) Core architecture
How to build on top of Jupyter’s protocols
Kyle Kelley (Netflix)
Kyle Kelley walks you through creating a new web application from the ground up, teaching you how to build on top of Jupyter's protocols in the process. Along the way, you'll learn about Jupyter's REST and streaming APIs, message spec, and the notebook format.
9:00am-12:30pm (3h 30m) Community, Training and education
An introduction to Julia in Jupyter
Jane Herriman (Julia Computing)
Jane Herriman uses Jupyter notebooks to show you why Julia is special, demonstrate how easy it is to learn, and get you writing your first Julia programs.
1:30pm-5:00pm (3h 30m)
Advanced Data Science
Bruno Gonçalves (JPMorgan Chase & Co.), Matt Brems (General Assembly)
This two-part tutorial presents a sequence of advanced topics in Data Science, based on using Jupyter.
9:00am-12:30pm (3h 30m) Data visualization, Training and education, Usage and application
Network and graph analysis with Jupyter notebooks
Noemi Derzsy (AT&T Labs)
Networks, also known as graphs, are one of the most crucial data structures in our increasingly intertwined world. Social friendship networks, the web, financial systems, and infrastructure are all network structures. Noemi Derzsy explains how to generate, manipulate, analyze, and visualize graph structures that will help you gain insight about relationships between elements in your data.
1:30pm-5:00pm (3h 30m) Training and education
I Do, We Do, You Do: Supporting active learning with notebooks
Rachael Tatman (Kaggle)
Rachael Tatman offers practical introduction to incorporating Jupyter notebooks into the classroom using active learning techniques.
9:00am-12:30pm (3h 30m) Community, Reproducible research and open science, Training and education
Preparing your Jupyter notebook for computationally reproducible publication: A hands-on BYONotebook tutorial for researchers
April Clyburne-Sherin (Code Ocean)
April Clyburne-Sherin walks you through preparing Jupyter notebooks for computationally reproducible publication. You'll learn best practices for publishing notebooks and get hands-on experience preparing your own research for reuse, creating documentation, and submitting your notebook to share.
1:30pm-5:00pm (3h 30m)
JupyterLab tutorial
Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS)
JupyterLab—Jupyter's new frontend—goes beyond the classic Jupyter Notebook, providing a flexible and extensible web application with a set of reusable components. Jason Grout and Matthias Bussonnier walk you through using JupyterLab, explain how to transition from the classic Jupyter Notebook frontend to JupyterLab, and demonstrate JupyterLab's new powerful features.
12:30pm-1:30pm (1h)
Break: Lunch
5:00pm-6:30pm (1h 30m)
Jupyter Poster Session
The Jupyter Poster Session is an opportunity for you to discuss your work with other attendees and presenters. Posters will be presented Wednesday evening in a friendly, networking setting so you can mingle with the presenters and discuss their work one on one.
7:00pm-10:00pm (3h)
Jupyter Dine-Around
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. This is a great chance to make new connections and sample some of the great cuisine New York City has to offer.
8:00am-9:00am (1h)
Break: Morning Coffee