The first step, asking the right question, can often be the most difficult part of your machine learning adventure. The purpose of any ML project is to answer a question. "Who wrote this", "What is this", "What will the price be", "What patterns are there"? Having a concrete question you're focused on answering allows you to clearly define your objective function and helps you identify the data you need to actually do the work. Without asking the right question, your team could sink countless hours into collecting, refining, and modeling data that produces a useless product.

Every year we see the same endless product cycles for smartphones. While consumers like seeing phones with new designs, improved cameras and AR/VR capabilities, a silent revolution that has been unfolding since 1959 is now changing the world around us. Machine learning, a field evolved from the study of pattern recognition and computational learning theory in artificial intelligence, has leaped ahead in the last decade. Major improvements to data storage, processing power and accessibility of machine learning tools have served as catalysts of this silent revolution.