Pervasive and simpler access of mobile, social, and cloud have led to the unprecedented data generation and exponential information growth. Companies are hoovering up the data under the notion that it could become a valuable commodity resulting in business success, but all of that data can be a bane or boon depending on how prepared a company is in dealing with the data it is hoarding. The data can become a bane if the data is not protected and leveraged successfully leading to lost business opportunities; impact the bottom line by retaining outdated data or irrelevant information and ongoing governance. It can be a boon if it is harnessed to provide rich, actionable insights that can aid in securing and protecting from cyber threats, increasing business efficiencies, and creating a market differentiator.
Machine Learning is the ability that allows computers to learn through pattern recognition without explicitly being programmed with preset rules. Even though machine learning is not a new concept and the algorithms have been around for a long time, there is a renewed momentum due to the cheaper and more powerful computational processing and affordable data storage – leading to the ability to automatically apply complex mathematical calculations and produce models that can analyze bigger, more complex data and deliver faster, more accurate results on a very large-scale. Machine learning has become indispensable in transforming big data into information that is accurate without any false positives to provide actionable insights. It is used in several well-known consumer and business services such as online searches, recommendations of products, fraud detection to name a few and is imperative for future innovations like driverless cars.
Machine learning intelligence is ingrained in Microsoft’s cloud services, enterprise solutions, and consumer services. Here are a few examples of machine learning in Microsoft’s cloud services and solutions:
Microsoft analyzes 1.3+ billion authentications and 400 billion emails for spam and malware. Microsoft security teams correlate large-scale critical security events with behavior
and anomaly based search queries using supervised machine learning to provide real time protection from emerging and constantly evolving threats. Threat intelligence is incorporated
into Microsoft’s services offerings and solutions that provide multi layered protection via Microsoft Exchange Online Protection, Advanced Threat Protection (ATP), Azure Identity
Protection, Azure Privileged Identity Protection, Windows Defender Antivirus, Device Guard, Windows Device Advanced Threat Protection, and Azure Security Center.
- Data Governance
Office 365 Advanced Data Governance applies machine learning, predictive coding, and text based analytics to intelligently deliver proactive policy recommendations; classify data based on automatic analysis of factors like the type of data, its age and the users who have interacted with it; and take action, such as preservation or deletion. This automatic analysis reduces costs and alleviates the biggest impediments that organizations face with eDiscovery of large amounts of data.
- Enhance Business Productivity
Microsoft is constantly enhancing features in Office 365 and incorporating machine learning features to increase business productivity. Machine learning is responsible for the focused inbox, so the users do not have to wade through emails that are not important, Editor in word to help improve user’s writing, QuickStarter for PowerPoint and Sway to help the user with curated outlines for any topic, Designer for PowerPoint to automatically turn the user’s slides to professionally designed slides, PowerMap in Excel to automatically convert the data, and Relationships Assistant for Dynamics 365 to suggest the next action.
Microsoft Office Graph, an engine that uses machine learning to determine from the vast data across various Microsoft cloud services to detect relationships, and statistics that provide relevant content to users based on user interactions. These new contextual insights enhance end-user experience and lead to increased efficiencies as the users will spend less time hunting for the data.
MyAnalytics, by analyzing the end user’s interactions with different Office 365 services, Microsoft is able to offer some key insights into the user’s work habits via a personal productivity dashboard. MyAnalytics allows the user to stay up-to-date with important contacts, share key metrics with a mentor and help prioritize the time spent with different groups.
In addition to embedding machine learning capabilities to provide rich insights in their services and products, Microsoft is empowering organizations by not only providing access to data, but to the machine learning insights about the data as well as to the ability to build intelligence to in-house business apps an organization may be developing.
- Microsoft Graph API
A single HTTP-based API that can be used to access, combine, and build workflows and applications with data across a broad range of the Microsoft Cloud Services – Azure, Office 365, and Intune through user’s choice of language or application framework. Graph API enables creation of simple applications that auto schedules meetings or show the related files from various sources based on user’s relationships to complex solutions that measure employee engagement or create smart workflows that combine structure of an organization with a business approval process.
- Azure Machine Learning (Azure ML)
Azure ML ushers in the consumerization of machine learning capabilities allowing an organization to analyze and visualize the data set. Building an internal application to take advantage of the data set often takes weeks or months to code and engineer at scale and requires specialized expertise, but with Azure ML enabling predictive analytics is significantly easier for the developers without the hardware, software, deployment, and maintenance prerequisites. Through an integrated development environment called ML Studio, organizations can build data models through drag-and-drop gestures and simple data flow diagrams or drop existing R code or develop code using more than 350 R packages supported by ML Studio. Azure ML can also leverage APIs, custom web services published on the Azure Marketplace.
- Microsoft Machine Learning Services
A platform for integrating machine learning intelligence with the applications, developers can build learning capabilities into their own applications such as recommendations, sentiment analysis, fraud detection, fault prediction, and more via Microsoft Machine Learning Services. The advantage of bringing the intelligence closer to the data ensures that latency and risk are removed by moving the data out of the database and building machine learning models on possibly outdated data set. Applications run faster, even with extremely large data sets with integrated support R or Python.
- Machine Learning Frameworks
Microsoft’s machine learning framework Microsoft DMTK (Distributed Machine Learning Toolkit) tackles the challenge of large number of computational resources required to process large amounts of data sets by distributing machine learning jobs across a cluster of systems and running them in parallel.
Microsoft Computational Network Toolkit (CNTK) a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. CNTK supports parallelization across both multiple machines and multiple GPUs, regardless of where they are located.
Intelligence is ingrained in all of Microsoft’s offerings, contact New Signature if you want to harness this intelligence and create a market differentiator for your organization.