Now, it's time to explore some cool Big Data and Machine Learning solutions that have been built using Google Cloud. Then, you will get a chance to find a use case and explore it in your own activity. Keller Williams is a US real estate company. Keller Williams uses AutoML Vision to automatically recognize specific features of houses, like this house has a built-in bookcase. This helps agents get houses listed faster and buyers find houses that meet their needs. Neil Dholakia, Chief Product Officer of Keller Williams says that by training a custom machine learning model to recognize common elements of furnishings and architecture, customers can automatically search home listing photos for specific features, like granite countertops or even more general styles like, "Show me modern houses." This application of machine learning allows Keller Williams' realtors to quickly walk around a home and record a video, and use the object detection capabilities of AutoML Vision to find and tag key aspects of the home that customers might want to search on. A big benefit for their organization is that they already had many existing images and videos of home walk-throughs already. They simply fed them into the pre-built AutoML Vision model and customized it, all without writing a line of code. You will learn more about AutoML Vision and practice creating machine learning models with it later in this course. Ocado, the UK online only grocery supermarket used machine learning to automatically route emails to the department that needed to actually handle them. This avoids multiple rounds of reading and triaging those emails. With their old process, all the mails went to a central mailbox where the email was read, and then routed to the person or department that could handle it. Unfortunately, the central mailbox with somebody reading all the emails, that doesn't scale. So it led to long delays and poor user experience. So Ocado used machine learning, specifically the ability to read an email, to process natural language, to discover customer sentiment, and what the message was about so that they could route it immediately and automatically. One last use case. Kewpie manufactures baby food. In this case, quality is not necessarily a matter of safety, because the food itself is safe. But if baby food is discolored, it tends to get parents very concerned. So Kewpie turned to Google and our partner BrainPad to build a solution that leverages image recognition to detect low-quality or discolored potato cubes. The machine learning algorithm enabled them to free people up from the tiring work of inspection and focused on other more important work.