We compared Microsoft Azure Machine Learning Studio and TensorFlow based on our user's reviews in several parameters.
In summary, Microsoft Azure Machine Learning Studio is praised for its user-friendly interface, seamless integration with other Azure services, reliable performance, and excellent support and documentation. On the other hand, TensorFlow is valued for its versatility, efficiency, extensive library of tools, and user-friendly interface. Users appreciate the flexible pricing options of both platforms, with Microsoft Azure Machine Learning Studio offering reasonable setup costs and TensorFlow providing a variety of pricing options suited to different needs. However, users have identified areas for improvement in both platforms, such as enhancing the user interface and documentation for Microsoft Azure Machine Learning Studio, and improving ease of use, documentation, and performance optimization for TensorFlow.
Features: Microsoft Azure Machine Learning Studio is praised for its user-friendly interface, extensive range of tools and algorithms, seamless integration with Azure services, reliable and scalable performance, and excellent support and documentation. On the other hand, TensorFlow is highly valued for its versatility, usability, efficiency, extensive library of tools and functions, flexibility in building and training deep learning models, user-friendly interface, well-documented resources, efficient utilization of hardware resources, and pre-built models, algorithms, and visualization tools.
Pricing and ROI: The setup cost for Microsoft Azure Machine Learning Studio is reasonable, with users finding the licensing process straightforward. In comparison, TensorFlow offers flexible pricing options suited to different needs, with a straightforward setup cost that users find hassle-free. TensorFlow's licensing is perceived as fair and transparent, instilling confidence in its usage., User feedback indicates positive ROI for both Microsoft Azure Machine Learning Studio and TensorFlow. Azure ML Studio is praised for its reliability, user-friendliness, and seamless data integration, while TensorFlow users have reported significant value and favorable outcomes.
Room for Improvement: Microsoft Azure Machine Learning Studio could improve its user interface to be more user-friendly. It also needs better documentation and collaboration features. In contrast, TensorFlow could enhance its ease of use, installation process, and performance. It should provide more comprehensive tutorials, visualization capabilities, and debugging tools.
Deployment and customer support: In the user reviews for Microsoft Azure Machine Learning Studio, there is variability in the reported durations for deployment, setup, and implementation. Some users mention different time frames for these phases, while others suggest they occur within the same period. However, user reviews for TensorFlow indicate a wider range of durations, with deployment taking a few weeks or a month, and setup ranging from a few days to a month. This suggests that Azure Machine Learning Studio may have a more consistent or efficient process for establishing a new tech solution compared to TensorFlow., Microsoft Azure Machine Learning Studio offers excellent assistance and guidance, with prompt and efficient support. Users praise the reliable and knowledgeable customer service. TensorFlow also provides highly praised customer service, ensuring prompt and helpful responses and a knowledgeable support staff.
The summary above is based on 29 interviews we conducted recently with Microsoft Azure Machine Learning Studio and TensorFlow users. To access the review's full transcripts, download our report.
"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's most beneficial feature is its integration with Azure."
"Visualisation, and the possibility of sharing functions are key features."
"The most valuable feature of Azure Machine Learning Studio for me is its convenience. I can quickly start using it without setting up the environment or buying a lot of devices."
"The most valuable feature is its compatibility with Tensorflow."
"ML Studio is very easy to maintain."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"The solution facilitates our production."
"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."
"TensorFlow provides Insights into both data and machine learning strategies."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"Operability with R could be improved."
"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."
"The solution cannot connect to private block storage."
"In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"Microsoft should also include more examples and tutorials for using this product."
"The initial setup time of the containers to run the experiment is a bit long."
"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."
"The solution is hard to integrate with the GPUs."
"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 51 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while TensorFlow is rated 9.0. The top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". On the other hand, the top reviewer of TensorFlow writes "Effective deep learning, free to use, and highly stable". Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, Google Cloud AI Platform and Dataiku, whereas TensorFlow is most compared with Google Vertex AI, OpenVINO, IBM Watson Machine Learning, Hugging Face and Azure OpenAI. See our Microsoft Azure Machine Learning Studio vs. TensorFlow report.
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