From advancing medical research, diagnoses, and treatments, to increasing farm yields, the wonders of AI continue to surprise us with new possibilities. If your business is not already leveraging AI enabled solutions today, chances are it will soon.
According to IDC surveys, 67% of organizations globally have already adopted or plan to adopt AI. And many adopters have seen returns that meet or exceed expectations , leading many to increase spending on AI in the next two years.
AI centers around amplifying the unique cognitive ingenuity of humans (imagining, creating, reasoning, and problem solving) and marrying it with best traits from intelligent technology (comprehending, handling of extreme detail, calculation, memory and recall, and organizing).
We help our customers to build for their users a wide range of solutions that learn and form conclusions with imperfect data.
Empower your customers with solutions that interpret the meaning of data, including text, voice, and images.
We enable our customers our customers with solutions that interact with people in natural ways and delivering all the functionality across channels.
How do you teach a car to drive? For many self-driving car makers and artificial intelligence researchers, the answer starts with data and sharing.
Hear from luminaries from the world of artificial intelligence including: Chris Nicholson, CEO of Skymind; Liam Paull, founder of Duckietown; Karl Iagnemma, CEO and co-founder of nuTonomy; Mary “Missy” Cummings, professor in the Department of Mechanical Engineering and Materials Science at Duke University; and Francois Chollet, AI researcher and author of Keras.
We help our customers build, connect, deploy, and manage intelligent bots to naturally interact with your users on a website, app, Cortana, Microsoft Teams, Skype, Slack, Facebook Messenger, and more.
The following steps summarizes the high-level phases that lead to the creation of useful predictive AI model. For a detailed look at the model building process, see the section Applying the Microsoft Team Data Science Process later in this book.
In the Prepare Data phase, the data is collected from sources and prepared for use in training the model. During this phase, the data may be cleaned and de-duplicated, the contents of the data is understood and the data that is the most informative in predicting the outcome is selected.
In the Build Model phase, a subset of the prepared data (which contains both the input and the outcome) is fed into a machine learning or deep learning algorithm to train the model. Then performance of the model is measured against another subset of the prepared data (referred to as the test or evaluation data set), and the model is evaluated on how well it performed in predicting the outcomes described in the test data set.
In the deploy model phase, the created model file is typically copied to a location where it can be used by the AI application for making predictions. This step is typically performed by developers, or more specifically, DevOps engineers who are responsible for making sure the model is deployed correctly into production.