Microsoft will soon offer a service aimed at making machine-learning technology more widely usable. "We want to bring machine learning to many more people," Eron Kelly, Microsoft corporate vice president and director SQL Server marketing, said of Microsoft Azure Machine Learning, due to be launched in beta form in July. "The line of business owners and the marketing teams really want to use data to get ahead, but data volumes are getting so large that it is difficult for businesses to sift through it all," Kelly said. An offshoot of artificial intelligence, machine learning uses algorithms so that computers recognize behavior in large and streaming data sets. It can be superior to traditional forms of business intelligence in that it offers a way to predict future events and behavior based on past actions.
Enterprises today are adopting artificial intelligence (AI) at a rapid pace to stay ahead of their competition, deliver innovation, improve customer experiences, and grow revenue. AI and machine learning applications are ushering in a new era of transformation across industries from skill sets to scale, efficiency, operations, and governance. Microsoft Azure Machine Learning provides enterprise-grade capabilities to accelerate the machine learning lifecycle and empowers developers and data scientists of all skill levels to build, train, deploy, and manage models responsibly and at scale. Let's dive into these announcements in detail to see how Azure Machine Learning is helping individuals, teams, and organizations meet and exceed business goals. "By improving forecasting using Azure Machine Learning automated ML, we can reduce waste and ensure pizzas are ready for our customers. This will reduce the guesswork for our operators and allow them to spend more time focusing on other aspects of store operations. Rather than guessing how many pizzas to have ready, store operators are focusing on making sure every customer experience is an excellent one."
Although I'm down in Orlando, Florida for the SQL Sever Live! and Visual Studio Live! conferences, Microsoft is putting on its annual Connect(); developer event, up in Manhattan where I normally spend most of my time. And though I'm missing the live event itself, Microsoft was kind enough to brief my on a slew of data-related announcements the company is making at Connect() today. I cover them in detail here.
Across Visual Studio Code and Azure Notebooks, January brought numerous exciting updates to the AI and Machine Learning tooling for Python! The Python extension for VS Code first introduced an interactive data science experience in the last Oct update. With this release, we brought the power of Jupyter Notebooks into VS Code. Many feature additions have been released since, including remote Jupyter support, ability to export Python code to Jupyter Notebooks, etc. The most noticeable enhancement in the Jan 2019 update allows code to be typed and executed directly in the Python Interactive window.
Artificial intelligence (AI) workloads include megabytes of data and potentially billions of calculations. With advancements in hardware, it is now possible to run time-sensitive AI workloads on the edge while also sending outputs to the cloud for downstream applications. AI scenarios processed on the edge can facilitate important business scenarios, such as verifying if every person on a construction site is wearing a hardhat, or detecting whether items are out-of-stock on a store shelf. The combination of hardware, software, and AI models needed to support these scenarios can be difficult to organize. To remove this barrier, we announced a developer kit last year with Qualcomm, to accelerate AI inferencing at the intelligent edge.