The biggest advantage of Azure AI stack is the diversity where even a software developer with limited knowledge on machine learning and AI theories is able to develop intelligent apps using the pre-built intelligent components.
In this article we discuss the set of products, tools and services Microsoft offers for leveraging the power of AI in application development lifecycle. The biggest advantage of Azure AI stack is the diversity where even a software developer with limited knowledge on machine learning and AI theories is able to develop intelligent apps using the pre-built intelligent components. There are three main pillars in Microsoft AI platform.
Microsoft offers a variety of pertained ready to use AI services that can be easily combine with the existing application development life cycle. Most of these services are exposed as RESTful APIs which aligns with the micro services architecture. Here are the main services comes under this pillar.
When it comes to AI software architecture governance, it demands the flexibility in handling large amounts of data with high variation and velocity. Microsoft AI infrastructure caters both the aspects.
One of the major advantages in Microsoft AI stack is its adaptability for existing AI development tools and frameworks. VSCode and Visual Studio both provide AI tools which allows the developers to connect the Azure resources to the development environment. Developers have the ability to use their familiar open source machine learning and deep learning libraries with Azure machine learning without any hassle using the AI tools. The recently introduced one-stop portal Azure Machine Learning Studio (ml.azure.com) provides a managed interface for accessing all machine learning related resources and code on the Azure cloud.
In the upcoming articles we are discussing the use cases of these products and services and the ways you can adapt them for developing intelligent applications.
Treating your infrastructure as code is becoming more and more necessary these days. Writing these instructions becoming challenging too. In Azure we use ARM templates to define the resources and associate them with a deployment pipeline. But ARM templates are quite complicated and they are not everybody’s cup of tea.
Azure MachineLearning Service provides four main compute options each with a specific purpose attached to it. In this post we will go through each of those and see where we can occupy them in your ML experiments.
In the era of Industry 4.0 where data and predictive analytics players the major role, developing machine learning pipelines have become an essential in intelligent application development.