We compared Microsoft Azure Machine Learning Studio and Google Vertex AI based on our user's reviews in several parameters.
Microsoft Azure Machine Learning Studio is praised for its excellent support and documentation, flexible pricing, and positive ROI. On the other hand, Google Vertex AI stands out for its advanced machine learning capabilities, seamless integration, and efficient model training. While Microsoft Azure focuses on user-friendly interface, Google Vertex AI wins with scalability and reliability.
Features: Microsoft Azure Machine Learning Studio excels in its user-friendly interface, wide 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 Vertex AI stands out with advanced machine learning capabilities, efficient and automated model training, seamless integration with existing infrastructure, and the ability to handle large datasets. Users also appreciate its user-friendly interface, scalability, and reliability.
Pricing and ROI: Microsoft Azure Machine Learning Studio offers flexible pricing options with reasonable setup costs. The licensing process is straightforward and easy to understand. On the other hand, Google Vertex AI's pricing is considered reasonable and competitive. The setup process is straightforward and hassle-free, requiring minimal effort. The licensing is praised for its flexibility and accommodation to different business needs., Microsoft Azure Machine Learning Studio has shown positive ROI with cost savings and improved efficiency, while Google Vertex AI has enhanced productivity, optimized business processes, and delivered tangible results.
Room for Improvement: Microsoft Azure Machine Learning Studio users have mentioned the need for a more intuitive user interface, better documentation for learning, improved collaboration features, and seamless integration with other tools. On the other hand, Google Vertex AI users have expressed the need for better customization options, more comprehensive documentation, and enhanced customer service support.
Deployment and customer support: The user feedback for Microsoft Azure Machine Learning Studio indicates varying durations for deployment, setup, and implementation phases. In contrast, feedback for Google Vertex AI mentions both deployment and setup taking either three months or a week, highlighting potential differences in the interpretation of these terms., Microsoft Azure Machine Learning Studio offers excellent assistance and guidance, with prompt, knowledgeable, and efficient support. Users found their customer service reliable and helpful. Google Vertex AI also provides commendable and satisfying customer service, with reliable, prompt, and professional assistance and guidance. Users have expressed contentment with their helpfulness and effectiveness.
The summary above is based on 28 interviews we conducted recently with Microsoft Azure Machine Learning Studio and Google Vertex AI users. To access the review's full transcripts, download our report.
"It provides the most valuable external analytics."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The solution facilitates our production."
"The product's standout feature is a robust multi-file network with limited availability."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"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."
"The solution is very easy to use, so far as our data scientists are concerned."
"It's easy to use."
"The UI is very user-friendly and that AI is easy to use."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."
"The speed of deployment should be faster, as should testing."
"Using the solution requires some specific learning which can take some time."
"I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else."
"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."
"They should have a desktop version to work on the platform."
"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."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 50 reviews. Google Vertex AI is rated 8.4, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Google Vertex AI writes "A user-friendly platform that automatizes machine learning techniques with minimal effort". 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 Vertex AI is most compared with Azure OpenAI, Hugging Face, Amazon SageMaker, TensorFlow and AWS Machine Learning, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Azure OpenAI, TensorFlow, Google Cloud AI Platform and Dataiku Data Science Studio. See our Google Vertex AI vs. Microsoft Azure Machine Learning Studio report.
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