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 of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"We have many use cases for the solution, such as digitalizing records, a chatbot looking at records, and being able to use generative AI on them."
"Azure OpenAI is very easy to use instead of AWS services."
"The product saves a lot of time."
"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."
"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."
"The high precision of information extraction is the most valuable feature."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"The solution is very easy to use, so far as our data scientists are concerned."
"The solution's most beneficial feature is its integration with Azure."
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"The initial setup is very simple and straightforward."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"Our customers are worried about data management, ethical, and security issues."
"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."
"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."
"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."
"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."
"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."
"Technical support could improve their turnaround time."
"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."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated."
"Integration with social media would be a valuable enhancement."
"The solution's initial setup process is complicated."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Azure OpenAI is ranked 2nd in AI Development Platforms with 23 reviews while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 51 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.
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