Agile Data Preparation & Exploration for Cloud Machine Learning

However, despite the hype around data science, artificial intelligence, and machine learning, much of the work is still cloistered away on disjointed data science teams. Every company wants to use it, but few know how. In fact, a report coming out of MIT Sloan showed that while 85% believed AI would give them a competitive advantage, only 20% of the respondents were actually using it. Why is AI/ML so hard to implement? Much of the challenge comes down to the manual aspects of the machine learning analytic cycle – accessing data, preparing data, exploration and feature engineering, model validation and finally operationalization.