We compared Microsoft Azure Machine Learning Studio and Google Cloud AI Platform based on our user's reviews in several parameters.
Microsoft Azure Machine Learning Studio offers excellent support and documentation, flexible pricing options, and positive ROI. There are suggestions for improving the user interface, collaboration features, and documentation. Google Cloud AI Platform provides robust machine learning capabilities, seamless integration with Google services, exceptional customer service, and positive ROI. Users have requested better documentation, flexibility, and integration with other Google services.
Features: Microsoft Azure Machine Learning Studio is valued for its user-friendly interface, broad range of tools and algorithms, seamless integration with other Azure services, reliable and scalable performance, and excellent support and documentation. On the other hand, Google Cloud AI Platform is praised for its robust machine learning capabilities, impressive scalability, advanced AI models and tools, integration with Google services, intuitive interface, and ability to handle large workloads efficiently.
Pricing and ROI: Microsoft Azure Machine Learning Studio offers flexible pricing options with reasonable setup costs. Users have found the licensing process to be straightforward. Google Cloud AI Platform provides cost-effective setup, with minimal and straightforward setup costs. Users appreciate its competitive pricing and flexible licensing options., Microsoft Azure Machine Learning Studio has shown positive ROI with cost savings, improved efficiency, and increased productivity. It offers seamless integration of data sources and easy visualization. On the other hand, users of Google Cloud AI Platform have reported achieving improved productivity and efficiency, along with valuable insights for data-driven decisions.
Room for Improvement: Microsoft Azure Machine Learning Studio users have identified a need for a more intuitive user interface, better documentation, improved collaboration features, and seamless integration with other tools. On the other hand, Google Cloud AI Platform users have requested improved documentation, more flexibility and customization options, better integration with other Google Cloud services, and enhanced performance for handling larger datasets.
Deployment and customer support: When comparing the user reviews, it is noted that both Microsoft Azure Machine Learning Studio and Google Cloud AI Platform have similar phases such as deployment, setup, and implementation. However, Azure users experienced variations in the duration, indicating that these processes may occur at different times. On the other hand, users of the Google Cloud AI Platform mentioned a longer timeframe for deployment but suggested that deployment and setup may refer to the same period., Microsoft Azure Machine Learning Studio offers reliable and efficient customer service, addressing user needs promptly. On the other hand, Google Cloud AI Platform provides exceptional customer support, with a responsive and knowledgeable team.
The summary above is based on 29 interviews we conducted recently with Microsoft Azure Machine Learning Studio and Google Cloud AI Platform users. To access the review's full transcripts, download our report.
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"The initial setup is very straightforward."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"I find Microsoft Azure Machine Learning Studio advantageous because it allows integration with Titan Scratch and offers an easy-to-use drag-and-drop menu for developing machine learning models."
"It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"The solution is scalable."
"ML Studio is very easy to maintain."
"The initial setup is very simple and straightforward."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The UI is very user-friendly and that AI is easy to use."
"The solution can be improved by simplifying the process to make your own models."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"Customizations are very difficult, and they take time."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"The initial setup was straightforward for me but could be difficult for others."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."
"Integration with social media would be a valuable enhancement."
"n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"The interface is a bit overloaded."
"The data cleaning functionality is something that could be better and needs to be improved."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Google Cloud AI Platform is ranked 6th in AI Development Platforms with 7 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 53 reviews. Google Cloud AI Platform is rated 7.8, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Google Cloud AI Platform writes "An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data". 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 AI Platform is most compared with Azure OpenAI, IBM Watson Machine Learning, Google Vertex AI, Hugging Face and Amazon SageMaker, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Dataiku. See our Google Cloud AI Platform vs. Microsoft Azure Machine Learning Studio report.
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