Microsoft Azure is poised to revolutionize IT architectures with its cloud computing platform. Its data center services will allow organizations to vastly reduce the costs of deploying and maintaining their infrastructure by moving their data and applications into a geographically distributed environment.
With this cloud platform, software developer Tom Keane explains, Microsoft enables customers to execute on several scale levels seamlessly. These, from individual workstations to large-scale distributed systems within minutes for these mission engineering projects. Tom Keane is the corporate vice president of Mission Engineering for Microsoft Azure. He is responsible for defining product strategy, working with customers and partners.
This, to understand their needs, and determine the technical direction necessary to deliver on them. Before joining Microsoft,he was director of research at HP Labs, focused on sustainable IT as it impacts enterprise customers. Tom Keane has a Ph.D. in Computer Science from the University of Massachusetts Amherst. He also holds an MBA from MIT’s Sloan School of Management and a BS in Computer Science from UMass Amherst.
- Analytics for A Massive Confluence of Data
The size of data is growing at an exponential rate, as we all know. So, too, is the complexity of the analysis of this data. There has been a parallel increase in the development of software frameworks that allow for more complex and higher-value data analyses. According to Tom Keane, these include machine learning algorithms and deep learning models for dealing with challenges such as:
Image recognition, speech recognition, language translation, and other areas requiring the ability to detect increasingly subtle patterns in large datasets. Azure Machine Learning is a new offering that provides a quick, easy-to-use and cost-effective way for companies to access the power of these machine learning models. Tom Keane finally states how it allows enterprises to quickly integrate and analyze their data sets to gain insights into opportunities, threats, trends, and user behaviors.