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 APIs are valuable."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"The UI is very user-friendly and that AI is easy to use."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The interface should be more user-friendly."
"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."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
"It would be nice if the product offered more accessibility in general."
"Microsoft should also include more examples and tutorials for using this product."
"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."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"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."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Google Cloud Datalab is ranked 13th in Data Science Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 47 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, Domino Data Science Platform and Qlik Sense, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio report.
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