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 most valuable feature is the ALM."
"Azure OpenAI is easy to use because the endpoints are created, and we just need to pass our parameters and info."
"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."
"Generative AI or GenAI seems to be the best part of the solution."
"The product's initial setup phase was pretty easy."
"The product saves a lot of time."
"Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed."
"My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context."
"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 most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"The product's standout feature is a robust multi-file network with limited availability."
"The solution is very easy to use, so far as our data scientists are concerned."
"Visualisation, and the possibility of sharing functions are key features."
"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."
"It helps in building customized models, which are easy for clients to use."
"Deployment was slightly complex for me to understand."
"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."
"Our customers are worried about data management, ethical, and security issues."
"We are awaiting the new updates like multi-model capabilities."
"I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator."
"The product must improve its dashboards."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"There is room for improvement in their support services."
"They should have a desktop version to work on the platform."
"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."
"The solution should be more customizable. There should be more algorithms."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"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 interface is a bit overloaded."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Azure OpenAI is ranked 2nd in AI Development Platforms with 24 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 53 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. See our Azure OpenAI 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.