We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"All of the features of this product are quite good."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The most valuable feature is data normalization."
"The UI is very user-friendly and that AI is easy to use."
"The solution is very fast and simple for a data science solution."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"The interface is very intuitive."
"The interface should be more user-friendly."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The data cleaning functionality is something that could be better and needs to be improved."
"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."
"The solution should be more customizable. There should be more algorithms."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
"Integration with social media would be a valuable enhancement."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"The data preparation capabilities need to be improved."
"When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly."
"From a developer's perspective, I find the price of this solution high."
"The licensing cost is very cheap. It's less than $50 a month."
"There is a license required for this solution."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
Earn 20 points
Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Google Cloud Datalab is ranked 19th in Data Science Platforms with 1 review while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 16 reviews. Google Cloud Datalab is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Google Cloud Datalab writes "Stable, feature-rich, and easy to set up". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Google Cloud Datalab is most compared with Databricks, IBM Watson Studio, MathWorks Matlab, Cloudera Data Science Workbench and KNIME, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, IBM Watson Studio, Dataiku Data Science Studio, Alteryx and Amazon SageMaker.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.