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.
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The initial setup is very straightforward."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"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 solution is able to read 90% of the documents correctly with a 10% error rate."
"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."
"ML Studio is very easy to maintain."
"Their support is helpful."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"It's a great option if you are fairly new and don't want to write too much code."
"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 initial setup was straightforward for me but could be difficult for others."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"Customizations are very difficult, and they take time."
"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."
"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."
"The solution can be improved by simplifying the process to make your own models."
"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."
"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."
"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."
"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."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
"The product must improve its documentation."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"There should be data access security, a role level security. Right now, they don't offer this."
"The interface is a bit overloaded."
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 51 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 IBM Watson Machine Learning, Azure OpenAI, 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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