Data science encapsulation and deployment with Anaconda Project and JupyterLab (sponsored by Anaconda Powered by Continuum Analytics)

Description

Traditional data science project deployments involve lengthy and complex processes to deliver secure and scalable applications in enterprise environments. As a result, data scientists must spend a nontrivial amount of time setting up, configuring, and maintaining deployment infrastructure.

Christine Doig offers an overview of the Anaconda Project, an open source library created by Continuum Analytics that delivers lightweight, efficient encapsulation and portability of data science projects. A JupyterLab extension enables data scientists to install the necessary dependencies, download datasets, and set environment variables and deployment commands from a graphical interface. The Anaconda Project abstraction combined with JupyterLab empowers data scientists to quickly iterate in the entire analytics process, reducing iteration time from data analysis, application development and deployment to production.

Topics include:

• Encapsulating your data science with Anaconda Project
• Using Anaconda Project with JupyterLab
• Reproducing your data science analyses, sharing data science projects with others, running projects across different platforms, and deploying data science applications with a single click
• Deploying self-service notebooks, interactive applications, dashboards, and machine learning models

This session is sponsored by Anaconda Powered by Continuum Analytics.