We often start our data science discussions assuming data is ready to analyse and able to give us a predictive model that we can rely on. But little thought is given to practices enabling us to continually add value.

Modern software development is agile, but more and more our software relies on predictive models whose development is decidedly not agile. Often overlooked in data science discussions are documentation, model creation and retraining, testing and monitoring production.  

In this free virtual session, see how DevOps best practices can be applied to data science and machine learning to meet all these requirements. You will gain an understanding of the practices, and the tools available to enable you to get the most out of your Data Science efforts.

Agenda

Speaker Presentations - 30 mins
Audience Q&A - 15 mins