DSS sponsored by DataRobot: "Building Model Factories with DataRobot"

Building Model Factories with DataRobot using Python and R

DataRobot, a machine learning automation platform that allows users of all skill levels to make better predictions faster. Incorporating hundreds of the most powerful open source machine learning algorithms from R, Python, SparkML, Vowpal Wabbit, XGboost, H2O, Tensorflow and other libraries, the DataRobot platform automates, trains and evaluates predictive models in parallel, delivering more accurate predictions at scale.

Many business problems require a large number of models to be built and refreshed regularly. Ideally, you would automate this entire process from monitoring existing models, collating new datasets, building and selecting models, then deploying them.

In this presentation, we will demonstrate how DataRobot can be used in the later stages of the automation pipeline programmatically with no need for a GUI!

• Introduction to DataRobot Python R APIS
• building and selecting models in with Python and R
• seamlessly deploying them programmatically
• scoring models via Rest API

You will learn how scale the modelling process and use DataRobot as a machine learning workhorse for designing your own automated model deployment factory.

Skill level - All Data Scientists

About the Speaker - Dr John Hawkins

Dr John Hawkins completed his PhD in Applied Machine Learning at the University of Queensland and then conducted research at Universities in Australia and Germany building predictive models of protein behaviour and function. He left academia to work as a consultant applying machine learning and data science for multiple businesses in Australia: AdTech startup Big Model, the Commonwealth Bank of Australia and Channel 9. He now works for Boston start-up DataRobot helping Australian businesses utilise their automated machine learning platform.