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Microsoft Azure is a top public cloud provider offering services in on-premises and cloud versions. This often makes it easier, especially for Microsoft-based organizations, to migrate to the Azure platform. However, migrating to the Azure cloud is not without challenges. From dealing with the mental paradigm shift to managing dependencies and system downtimes during migration, there are many aspects that need careful consideration and planning. Working with an experienced migration partner, you can easily overcome such challenges and effect a smooth transition to achieve your goals for agility, scalability, and availability with the Azure cloud.
Connect IoT devices to the application backend with bi-directional communication and collect untapped data for your business intelligence reports.
Empower your business with real-time intelligence. Easily process audio, video, IoT, or non-IoT data without any infrastructure to manage.
Use Microsoft Bot Framework and bot builder tools to fast-track your chatbot development. Add language understanding to the mix and you will have a great conversationalist.
Engage users with systems that understand and respond to natural language. Well-trained and continually updated models will correctly interpret intent and serve accurate results.
Populate your analytics and reporting systems with data consolidated from multiple sources using Data Factory and its connectors.
Bring interactivity into your reporting applications. Use embedded analytics to deliver complex data in easy-to-comprehend visuals.
One of the common reasons to migrate to a cloud platform is scalable storage. Massively scalable and highly available, secure storage options accessible from anywhere could be a game-changer for your application and its users. Based on the nature of data to be stored, we’ll help you choose and configure from the range of storage options Azure provides.
Our client operates in the medical field and wanted a monitoring system for hospitals to monitor patients confined to bed. They envisioned a sensor-based system to alert nursing staff when a bedridden patient needs to be moved or turned over to prevent pressure sores from developing.
The developed IoT solution has sensors attached to hospital beds to detect patient position. Data from these sensors are collected at the Azure IoT hub, which facilitates two-way communication with the sensing devices. Ingested data is analyzed using Azure Stream analytics and then fed into a Power BI connector for reporting. Stream analytics triggers an Azure function in the case of an anomaly (patient has been in one position for too long). The Azure function will save the anomaly in the database as well as trigger notification to various configured channels. As a result, the nursing staff will receive an alert to move the patient.
The architecture includes Azure Device Provisioning Service (DPS) for auto-registration of the IoT devices. DPS is a helper service for IoT hub that allows just-in-time provisioning to the right IoT hub enabling customers to provision millions of devices in a secure manner without human intervention.
The chat assistant was developed to cater to two main use-cases—to handle frequently asked questions and for task automation. Azure chatbot service was integrated with LUIS for language understanding and QnA Maker to realize the bot. The chatbot was integrated with multiple channels such as Skype for business and the client’s internal web application.
Our client had troves of information stored in FAQ documents, but querying this manually to find answers was not an efficient process. They wanted an intelligent system that would quickly scour the knowledge base and respond to user queries. The chatbot integrated with QnA Maker solved this problem.
Admin staff upload the FAQ in the QnAMaker portal. When users type/ask queries to the chatbot, the bot provides appropriate responses after crawling through the uploaded answers. The built-in intellisense of the QnAMaker service helps in figuring out relevant responses even when there are spelling errors or syntactical differences in the queries.
The chat assistant was also integrated with many internal services (Jira, O365, leave management system) to enhance employee productivity. It helps users with mundane jobs such as updating task status, sending reminders, checking user calendar, listing assigned tasks, or applying for leave.
To overcome the problem of forgetting passwords and to avoid phishing attacks that undermine security, our client decided to go password-less. They decided on a password-less authentication framework for their various applications where users would be authenticated through facial recognition.
We built them a centralized authorization framework that uses facial recognition as validation for the login mechanism. The image from the webcam is analyzed to detect the face and the user is identified via the Azure Face API. This is then validated against the person ID to verify the user.
The system is extensible (new authentication techniques can be integrated easily) and provides a uniform login experience for users across applications. It also acts as an SSO framework respecting the OAuth2/OpenID Connect protocols.