Over the years, various companies have rolled out all sorts of “[Fill in the blank]-as-a-Service” to meet tech pros’ increasing appetite for cloud-based tools. Now, in the age of artificial intelligence (A.I.) and machine learning, we have a new one: “Deep Learning-as-a-Service,” or “DLaS,” from IBM. “It embraces a wide array of popular open source frameworks like TensorFlow, Caffe, PyTorch and others, and offers them truly as a cloud-native service on IBM Cloud, lowering the barrier to entry for deep learning,” reads IBM’s official description of the new service. “It combines the flexibility, ease-of-use, and economics of a cloud service with the compute power of deep learning.” You can see the market for this: more and more companies want to get involved in A.I., but fewer have the resources and connections to hire experts to build and tweak the underlying algorithms—and that’s even before you get into the hardware costs (something like deep learning demands a lot of computational power, which means buying a lot of storage and GPUs, for example). If you’re running a startup that needs to train software in how to automate some process, you’re going to be very interested in a service that allows you to purchase deep-learning capability without needing to worry about infrastructure or having a specialized team. Deep learning centers on the construction of neural networks, which are fed enormous datasets. Over multiple iterations, these neural networks become better at handling tasks that involve those datasets. For example, if you uploaded millions of images of human faces to a neural network, and began training it through algorithms to identify noses or eyes, it would eventually get quite good at that task. You can see the implications for businesses of all types: a neural network can quickly automate tasks that would take human beings a longer time to accomplish. This isn’t IBM’s first attempt at taking a complicated, potentially expensive technology and molding it into an on-demand service. Last year, it did the same thing with Watson, its A.I. platform. The goal: allow both experts and “regular” employees to build artificial intelligence into tasks. “We have just released in general availability a Watson-as-a-service toolset,” Ryan Anderson, Architect in Residence (Watson West) & Cognitive Prototypes for IBM, told Dice at the time. “There’s a real nuts-and-bolts data science approach, but there are people building with cognitive who are more on the marketing and brand side who don’t care what’s going on under the covers, and they just want the API to return the information.”     In theory, even employees who aren’t great at coding can use this Watson-based service to build things like chatbots that interact with customers. That’s not quite the same audience that would use IBM’s deep-learning tool, though; by the time you’re trying to build a neural network, you almost certainly have some background in artificial intelligence and machine learning. Nor is IBM the only company trying to democratize A.I. For example, Google recently unveiled Cloud AutoML, a tool for automating the creation of customized machine-learning models. The first product on the new platform, Cloud AutoML Vision, will offer companies and developers a drag-and-drop interface for building image-recognition models. As A.I. penetrates more facets of daily life, expect more tech companies to issue tools that everyday employees can use, not just A.I. experts.