Data Citizens Should Use AzureML To Embrace AI Adoption Quickly
Microsoft is named a Leader in the 2021 Gartner Magic Quadrant for Cloud AI Developer Services.*
Azure is the cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services. Azure Machine Learning is a fully managed cloud service to build, deploy and share the predictive analytics solution. It provides tools such as AzureML studio to create complete machine learning solutions in the cloud.
These are some main differentiators of AzureML that can greatly benefit Data Scientists and Data Citizens:
1. Cloud-based predictive analytics Service: Without any local setup, you can quickly create models as well as deploy them using AzureML studio. One can do this without writing a single line of code for basic ML experiments.
2. Quick model creation and deployment: AzureML lets you build various models very efficiently and allows the model to be deployed as web services almost instantly. Any machine learning library finds its strength in the prebuild modules and algorithms. AzureML scores heavily here as well against some of the other such service providers and comes with a large library of pre-built machine learning algorithms and modules.
3. Customization: If you are a data science developer and have pre-built models, AzureML will work for you as well. It allows the developers to extend the models with custom-built R and Python code. You can also create the custom components of your logic that you can use in Designer for drag-and-drop use in the canvas.
Educating employee with data literacy increases the chances of AI adoption. Without coding experience, Data Citizens can leverage machine learning in their domains. AzureML can prove to be an enabler to achieving business goals.
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