We compared Microsoft Azure Machine Learning Studio and Azure OpenAI based on our user's reviews in several parameters.
Microsoft Azure Machine Learning Studio offers a user-friendly interface, excellent support, and flexible pricing options. Users have highlighted the need for better documentation and collaboration features. Azure OpenAI, on the other hand, focuses on seamless integration, scalable resources, and robust machine learning capabilities. Users appreciate its affordable pricing, extensive support, and positive ROI. Areas for improvement include specific functions and enhancements.
Features: Microsoft Azure Machine Learning Studio offers a user-friendly interface, a wide range of tools and algorithms, seamless integration with other Azure services, reliable and scalable performance, and excellent support and documentation. In comparison, Azure OpenAI focuses on seamless integration with other Azure services, flexibility in resource scaling, robust machine learning capabilities, and extensive documentation and support.
Pricing and ROI: Azure Machine Learning Studio offers flexible pricing options with reasonable setup costs, according to user feedback. On the other hand, Azure OpenAI is positively regarded for its affordable pricing and minimal setup cost. Users find it cost-efficient and note the smooth setup process. Azure OpenAI also provides adaptable licensing options., The ROI of Microsoft Azure Machine Learning Studio includes cost savings, improved efficiency, and reliable predictions. Azure OpenAI offers increased efficiency, cost reduction, and valuable insights for better decision-making.
Room for Improvement: Microsoft Azure Machine Learning Studio: Users have identified areas for improvement, including enhancing the user interface, better documentation and guidance, improved collaboration features, and seamless integration with other tools. Azure OpenAI: Users have provided feedback on enhancing Azure OpenAI, including concerns regarding certain functions and suggested improvements.
Deployment and customer support: The user reviews for Microsoft Azure Machine Learning Studio indicate some variation in the durations for deployment, setup, and implementation phases, suggesting that these processes may occur at different times. In contrast, the reviews for Azure OpenAI suggest that deployment and setup are considered to be the same period and should not be evaluated separately., Microsoft Azure Machine Learning Studio's customer service is praised for being prompt, knowledgeable, and efficient. On the other hand, Azure OpenAI's customer service is regarded as highly appreciated, efficient, and reliable, ensuring a smooth user experience.
The summary above is based on 35 interviews we conducted recently with Microsoft Azure Machine Learning Studio and Azure OpenAI users. To access the review's full transcripts, download our report.
"The product saves a lot of time."
"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"Azure OpenAI is useful for benchmarking products."
"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."
"The product is easy to integrate with our IT workflow."
"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."
"The high precision of information extraction is the most valuable feature."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"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."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"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 AutoML is helpful when you're starting to explore the problem that you're trying to solve."
"MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The interface is very intuitive."
"The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."
"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"There are no available updates of information that are currently provided."
"Sometimes, the responses are repetitive."
"We are awaiting the new updates like multi-model capabilities."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"There should be data access security, a role level security. Right now, they don't offer this."
"Microsoft should also include more examples and tutorials for using this product."
"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."
"The price of the solution has room for improvement."
"I would like to see modules to handle Deep Learning frameworks."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"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."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Azure OpenAI is ranked 2nd in AI Development Platforms with 19 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 50 reviews. Azure OpenAI is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Azure OpenAI writes "Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions". 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". Azure OpenAI is most compared with Google Vertex AI, Amazon SageMaker, Hugging Face, Google Cloud AI Platform and IBM Watson Studio, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, TensorFlow, Google Cloud AI Platform and Dataiku Data Science Studio. See our Azure OpenAI vs. Microsoft Azure Machine Learning Studio report.
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