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.
This session is sponsored by Anaconda Powered by Continuum Analytics.
Christine Doig is a senior product manager and data scientist at Anaconda Powered by Continuum Analytics. Christine has 8+ years of experience in analytics, operations research, and machine learning in a variety of industries, including energy, manufacturing, and banking. An open source advocate, she has spoken at PyData, EuroPython, SciPy, PyCon, OSCON, and many other open source conferences. Christine holds an MS in industrial engineering from the Polytechnic University of Catalonia in Barcelona.
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