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 Explore AWS Machine Learning Platform using Amazon SageMaker (Day 2) to your personal schedule
9:00am Explore AWS Machine Learning Platform using Amazon SageMaker (Day 2) Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Concourse E
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 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 (New York University), Matt Brems (General Assembly)
Gramercy A
Gramercy B
Add JupyterLab tutorial to your personal schedule
1:30pm JupyterLab tutorial Jason Grout (Bloomberg), Matthias Bussonnier (UC Berkeley BIDS)
10:30am Morning Break | Room: Concourse Foyer
3:00pm Afternoon Break | Room: Concourse Foyer
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)
Explore AWS Machine Learning Platform using Amazon SageMaker (Day 2)
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Machine Learning and IoT projects are now common for enterprises and startups alike. These advanced technologies have been the key innovation engine for businesses such as Amazon Go, Alexa, and Robotics. In this hands-on workshop, we will explore the AWS Machine Learning Platform using project Jupyter-based Amazon SageMaker to build, train, and deploy ML/DL models to Cloud, and AWS DeepLens.
9:00am-5:00pm (8h)
Hands-On Data Science with Python (Day 2)
Zachary Glassman (The Data Incubator)
This course offers a foundation in building intelligent business applications using machine learning. We will walk through all the steps 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.
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 Julia, and get you writing your first Julia programs.
1:30pm-5:00pm (3h 30m)
Advanced Data Science
Bruno Gonçalves (New York University), 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, Reproducible research and open science, Training and education
Human in the loop: Understanding model interpretation with Jupyter and Skater
Pramit Choudhary (DataScience.com)
Just predicting the target labels for a data science use case is not enough. It's important to understand the why, what, and how of a given model’s behavior. Pramit Choudhary explores algorithms (post hoc and rule extraction) to faithfully interpret ML models globally and locally with Jupyter's interactiveness and Skater, an open source library to demystify the inner workings of ML models.
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 the new powerful features of JupyterLab.
10:30am-11:00am (30m)
Break: Morning Break
3:00pm-3:30pm (30m)
Break: Afternoon Break
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