We performed a comparison between Cloudera Data Science Workbench and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."The Cloudera Data Science Workbench is customizable and easy to use."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"Their web interface is good."
"Azure's AutoML feature is probably better than the competition."
"The solution is scalable."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"Auto email and studio are great features."
"Their support is helpful."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"The platform's integration feature could be better."
"I would like to see modules to handle Deep Learning frameworks."
"The solution's initial setup process is complicated."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"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."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"The product must improve its documentation."
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Cloudera Data Science Workbench is ranked 17th in Data Science Platforms with 2 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 49 reviews. Cloudera Data Science Workbench is rated 7.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". 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". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, Google Cloud Datalab and Alteryx, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform.
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