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.
"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."
"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."
"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."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"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."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
"The interface is very intuitive."
"The most valuable feature of Microsoft Azure Machine Learning Studio is the ease of use for starting projects. It's simple to connect and view the results. Additionally, the solution works well with other Microsoft solutions, such as Power Automate or SQL Server. It is easy to use and to connect for analytics."
"The solution is very fast and simple for a data science solution."
"It helps in building customized models, which are easy for clients to use."
"The product's standout feature is a robust multi-file network with limited availability."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"Customizations are very difficult, and they take time."
"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."
"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."
"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."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"The platform's integration feature could be better."
"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."
"I would like to see modules to handle Deep Learning frameworks."
"The price could be improved."
"The product must improve its documentation."
"They should have a desktop version to work on the platform."
"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."
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 49 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, Google Vertex AI, Azure OpenAI, Hugging Face and Amazon SageMaker, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Google Vertex AI, Azure OpenAI, TensorFlow and Dataiku Data Science Studio. See our Google Cloud AI Platform vs. Microsoft Azure Machine Learning Studio report.
See our list of best AI Development Platforms vendors.
We monitor all AI Development 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.