Abstract: 

Setting up a data science workspace can have its major challenges. These can include things like having the right software and libraries installed with the correct versions, having the appropriate hardware including memory and CPUs, and not to mention, the additional support if you need a GPU for your work. Once you do set it up, you may realize the next set of challenges like needing a coworker to run your code who has a totally different setup or needing to rerun an analysis later on and can't replicate your environment. Saturn Cloud is a data science platform that lets you do your work in the cloud in Python, R, Julia, and more. It requires minimal setup, doesn't lock you into any fixed pre-existing environments that someone else made, and allows you to use multiple GPUs, up to 4TB of ram, and distributed Dask clusters. In this demo, we'll cover the best practices for setting up data science environments, how to store them so you can replicate your code, and how to share a setup with your coworkers.

Bio: 

Dr. Jacqueline Nolis is a data science leader with 15 years of experience in running data science teams and projects at companies ranging from Airbnb to Boeing. She is the Chief Product Officer at Saturn Cloud where she helps design products for data scientists. Jacqueline has a PhD in Industrial Engineering and her academic research focused on optimization under uncertainty. Data science is also her hobby—like making an R package that mails physical postcards of your plots.