Analytics and Data Summit 2020

Augmented Analytics is the new buzzword. OAC offers access to ML techniques in few clicks. Not a single line of code has to be written: load a dataset, click through the screen to setup a ML model and few more clicks to train and apply it to any other dataset you could have. Machine Learning couldn't be as simple and easy of access as that. The easiest way to democratize ML. Anyone can do ML, without having to go through linear algebra, various theoretical concepts and code to implement it. But does it really work? Can something as accessible as that be as effective as machine learning done by code? Analysing the dataset, doing features engineering and finally training a model to be able to apply to new data later.This session will use the same exact dataset and compare how ML can be done in OAC and in Python (using an Apache Zeppelin or Jupyter notebook). The results of the ML model will be compared to see how the two methods compare in accuracy.