Microsoft Azure Machine Learning Studio Scalability

Alexandre Akrour
CEO at Inosense
We never went into production because we switched to Azure Databricks. We did, however, try some performance testing and tried scaling some resources. The scalability of this solution is quite easy. It is not difficult compared to some of the other tools that are available on Azure. We have only five users including data engineers, data scientists, and one data DevOps engineer who was working with us on creating all of the DevOps pipelines for deploying all of our models. View full review »
Software83c9
Software Engineer
Scalability, in terms of running experiments concurrently: Good. At max, I was able to run three different experiments concurrently. Scalability in terms of deploying models: Unknown, I never deployed on Azure. But I would guess REST API could probably easily handle a few K worth of hits per second, since that is how Microsoft is going to get paid. View full review »
ChrisPeddie
Tech Lead at a tech services company with 1,001-5,000 employees
Scalability for us was fine. We have about seven hundred users including customer service agents, sales agents, and cell phone account managers. It took us about twelve months to scale to this point, from an initial user base of seventy people, and we do not plan to increase usage further. View full review »
Find out what your peers are saying about Microsoft, Databricks, Knime and others in Data Science Platforms. Updated: November 2019.
382,196 professionals have used our research since 2012.
Danilo Faria
System Analyst at a financial services firm with 1,001-5,000 employees
No issues with scalability. View full review »
Find out what your peers are saying about Microsoft, Databricks, Knime and others in Data Science Platforms. Updated: November 2019.
382,196 professionals have used our research since 2012.
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