If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics in the cloud. Author Nooruddin Abbas Ali, principal solutions architect at MongoDB, brings you up to speed through industry use cases and end-to-end hands-on examples. At the time of writing this book, there was a lot of buzz about Artificial Intelligence (AI) and Machine Learning (ML). But how did we get from simple calculations to a chatbot having coherent conversations with other humans? From statistical extrapolation of sales figures to planning stocks based on predicted sales, enterprises have come a long way. This journey from data producing to data consuming and finally data driven is what I explore in this first chapter. Together I want to delve into the details of how data analytics has evolved over time, what is predictive analytics (PA) at its roots and its place in the analytical world. I then go on to discuss its role when it comes to data modeling and Machine Learning and the tools available today that can help us do just that. While Tensorflow with Python gives us a strong foundation on Machine Learning and its use for predictive analytics, it’s worthwhile to explore AIML services from major public cloud providers.