As computational models and statistical methods catch up with the sizable data sources available to today’s data science teams, this talk will introduce the importance of capturing, sharing and remixing data-intensive pipelines. From reproducing old workflows, to prototyping new machine learning against disparate data, the ability to combine executable environments with utility computing platforms has the opportunity to greatly accelerate the pace of innovation in ‘big data’ applications. This talk will outline some real world examples and benefits of remixing, reusing and programming for reproducibility.
Structure:Data
Thanks for attending! – | New York, NY