Or maybe your team has applications running on Azure that need to securely connect to machine learning models deployed by the data science team on Google Cloud Platform. It’s easy to conceive scenarios where development and data science teams end up exploring, prototyping, and deploying applications, databases, microservices, and machine learning models to multiple public clouds.
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For larger enterprises, supporting multiple clouds is almost inevitable, due to the herculean level of governance required to channel all development, data science, and shadow IT efforts to a single public cloud. Even so, there are several reasons why global businesses and larger enterprises determine that “being multicloud” is strategically important.