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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.

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.

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.

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.

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.

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.

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.

This two-part tutorial presents a sequence of advanced topics in Data Science, based on using Jupyter.

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.

Rachael Tatman offers practical introduction to incorporating Jupyter notebooks into the classroom using active learning techniques.

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.

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.

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.

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.