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.
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"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."
"It provides the most valuable external analytics."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
"The solution is very easy to use, so far as our data scientists are concerned."
"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 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."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"It helps in building customized models, which are easy for clients to use."
"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."
"It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"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."
"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."
"The regulatory requirements of the product need improvement."
"The solution cannot connect to private block storage."
"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 speed of deployment should be faster, as should testing."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"The product must improve its documentation."
"It could use to add some more features in data transformation, time series and the text analytics section."
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 49 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, Amazon SageMaker, Hugging Face, 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.
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.
