refers to the process of deploying R and Python models to Machine Learning Server in the form of
and the subsequent consumption of these services within client applications to deliver business results.
In this release, we further optimized web service request-response time to make it significantly faster. You can construct a
dedicated session pool
for a specific web service to preload models and code; this will greatly reduce the web service request-response time especially when the models are big.
The installation process is already taken care by using these Azure Marketplace Images (which come with Machine Learning Server pre-installed):
We will use ARM Template Custom Script Extensions and the new
Admin CLI feature
to automate One-Box/Enterprise Configuration.
As the name suggests, one web node and one compute node run on a single machine. This configuration is useful when you want to explore what it is to operationalize R/Python analytics using Machine Learning Server. It is perfect for testing, proof-of-concepts, and small-scale prototyping, but might not be appropriate for production usage.
In this configuration, multiple nodes are configured on multiple machines along with other enterprise features like High Availability, Active Directory Authentication, Secure Connectivity etc. This configuration can be scaled up or down by adding or removing nodes.
We have created the following 4 ARM Templates for easy deployment of One-Box and Enterprise Configurations: