We performed a comparison between Google Cloud Datalab and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The infrastructure is highly reliable and efficient, contributing to a positive experience."
"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"The interface is very intuitive."
"Their web interface is good."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"ML Studio is very easy to maintain."
"The solution is very fast and simple for a data science solution."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"The solution is very easy to use, so far as our data scientists are concerned."
"The initial setup is very simple and straightforward."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The product must be made more user-friendly."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"The interface should be more user-friendly."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"It could use to add some more features in data transformation, time series and the text analytics section."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"The initial setup time of the containers to run the experiment is a bit long."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Google Cloud Datalab is ranked 16th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. Google Cloud Datalab is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". 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 SPSS Statistics, Cloudera Data Science Workbench, KNIME and IBM SPSS Modeler, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio report.
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.