The interactive nature of Jupyter notebooks has made them indispensable tools for data scientists and AI researchers, facilitating exploratory data analysis, prototyping, and model development. Setting up remote computational environments with multiple data or model sources on cloud is slow, repetitive and complex. However, warm pools of preconfigured environments with adjustable runtime configurations can alleviate these concerns enabling data scientists to focus more on actual data science and less on infrastructure. In this presentation, speakers will present a need for a system to manage integrated environments to meet the challenges of running remote Jupyter kernels at scale. With emphasis on productivity and experience, a solution for maintaining warm pools of such environments is presented while highlighting its key prediction algorithms. The session will showcase a brief and simplified collaboration experience on JupyterLab with the warm-pool system of kernels.
Jialin Zhang is a software engineer with Notebooks team offering Jupyterlab as a service to data scientists engineers at Apple. Her previous experience includes stint at Microsoft and Expedia.
