Artificial intelligence is being embedded into products to save people time and money. Experts in many domains have already begun to see the results of this, from medicine to education to navigation. But these products are built using an army of data scientists and machine learning experts, and the rate at which these human experts can deliver results is far lower than the current demand. My lab at MIT, called Data to AI, wanted to change this. Recognizing the human bottleneck in creating these systems, a few years ago we launched an ambitious project: we decided “to teach a computer how to be a data scientist." Our goal was to create automated systems that can ask questions of data, come up with analytic queries that could answer those questions, and use machine learning to solve them—in other words, all the things that human data scientists do. After much research and experimentation, the systems we have developed now allow us to build end-to-end AI products that can solve a new problem in one day. In this talk, I will cover what these new technologies are, how we are using them to accelerate the design and development of AI products, and how you can take advantage of them to actually build AI products faster and cheaper.