In November 2017, Microsoft announced the integration of Azure Machine Learning and Azure IoT Edge. With this integration, all models created using Azure Machine Learning can now be deployed to any IoT gateway and devices with the Azure IoT Edge runtime and can run on very small footprint devices.
Using artificial intelligence (AI) and machine learning (ML) Apps, websites and bots with intelligent algorithms see, hear, speak, understand and interpret user needs through natural methods of communication. This is truly transformational for businesses today.
Real world issues and even businesses problems are being solved with Microsoft’s cognitive services. Image-processing algorithms smartly identify, caption and moderate pictures using computer vision APIs, Face and Emotion APIs, Content Moderator etc. most of which are available through an Azure subscription.
Mapping complex information and data in order to solve tasks such as intelligent recommendations and semantic searches are again, available on Azure for Predictive notifications of what customers need. Language understanding, text analytics, predictive web language models and even translator text for machine translation is highly available via an Azure subscription.
Hybrid Models using applications on Cloud and on Premise
In the current context, businesses understand how AI and ML are critical to help them scale up to futuristic and predictive scenarios. The challenge lies in knowing how to apply AI and ML to data that cannot make it to the cloud, for reasons such as data sovereignty, privacy, bandwidth etc. Machine learning enables computers to learn from data and experiences and to act without being explicitly programmed. Customers can build AI applications that intelligently sense, process, and act on information – augmenting human capabilities, increasing speed and efficiency, and helping organizations achieve more.
From the Microsoft archives, there are many use cases for the intelligent edge, where a model is trained in the cloud and then deployed to an edge device. A great example is predictive maintenance of equipment on an oil rig in the ocean. All the data from the sensors on the oil rig can be sent to a server on the oil rig, and ML models can predict whether equipment is about to break down. Some of that data can then be sent on to the cloud to get an overview of what’s happening across all oil rigs and for historical data analysis.
By extending the Azure stack on premise, businesses now have the option of building hybrid applications and adopting the Cloud on their terms. To know more about rapid business scale up and process consistency through purpose built integrated systems, contact us on firstname.lastname@example.org for your complimentary consultation.