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Microsoft Azure Machine Learning Studio vs TensorFlow comparison

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Featured Review
Find out what your peers are saying about Microsoft Azure Machine Learning Studio vs. TensorFlow and other solutions. Updated: January 2022.
563,780 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The interface is very intuitive.""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.""Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills.""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 its compatibility with Tensorflow.""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 solution is very easy to use, so far as our data scientists are concerned.""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."

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"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe.""Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful.""TensorFlow is a framework that makes it really easy to use for deep learning.""TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features.""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.""Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers.""The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing.""Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."

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Cons
"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.""I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system.""n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces.""I think they should improve two things. They should make their user interface more user-friendly. Integration could also be better. Because Microsoft Machine Learning is a Microsoft product, it's fully integrated with Microsoft Azure but not fully supported for other platforms like IBM or AWS or something else.""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.""The AutoML feature is very basic and they should improve it by using a more robust algorithm.""They should have a desktop version to work on the platform."

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"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models.""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.""It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible.""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.""JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow.""However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications.""There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves.""I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

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Pricing and Cost Advice
  • "The licensing cost is very cheap. It's less than $50 a month."
  • "There is a license required for this solution."
  • "I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
  • More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →

  • "TensorFlow is free."
  • "I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."
  • "We are using the free version."
  • "It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."
  • "I did not require a license for this solution. It a free open-source solution."
  • More TensorFlow Pricing and Cost Advice →

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    Questions from the Community
    Top Answer: 
    Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Top Answer: 
    The initial setup is very simple and straightforward.
    Top Answer: 
    The licensing cost is very cheap. It's less than $50 a month would costs for multiple users.
    Top Answer: 
    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… more »
    Top Answer: 
    It is open-source software. You don't have to pay all the big bucks like Azure and Databricks.
    Top Answer: 
    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… more »
    Ranking
    1st
    Views
    16,529
    Comparisons
    13,221
    Reviews
    14
    Average Words per Review
    484
    Rating
    7.7
    2nd
    Views
    1,983
    Comparisons
    1,726
    Reviews
    10
    Average Words per Review
    825
    Rating
    9.2
    Comparisons
    Also Known As
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

    It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

    Microsoft Azure Machine Learning Will Help You:

    • Rapidly build and train models
    • Operationalize at scale
    • Deliver responsible solutions
    • Innovate on a more secure hybrid platform

    With Microsoft Azure Machine Learning You Can:

    • Prepare data: Microsoft Azure Machine Learning Studio offers data labeling, data preparation, and datasets.
    • Build and train models: Includes notebooks, Visual Studio Code and Github, Automated ML, Compute instance, a drag-and-drop designer, open-source libraries and frameworks, customizable dashboards, and experiments
    • Validate and deploy: Manage endpoints, automate machine learning workflows (pipeline CI/CD), optimize models, access pre-built container images, share and track models and data, train and deploy models across multi-cloud and on-premises.
    • Manage and monitor: Track, log, and analyze data, models, and resources; Detect drift and maintain model accuracy; Trace ML artifacts for compliance; Apply quota management and automatic shutdown; Leverage built-in and custom policies for compliance management; Utilize continuous monitoring with Azure Security Center.

    Microsoft Azure Machine Learning Features:

    • Easy & flexible building interface: Execute your machine learning development through the Microsoft Azure Machine Learning Studio using drag-and-drop components that minimize the code development and straightforward configuration of properties. By being so flexible, the solution also helps build, test ,and generate advanced analytics based on the data.
    • Wide range of supported algorithms: Configuration is simple and easy because Microsoft Azure ML offers readily available well-known algorithms. There is also no limit in importing training data, and the solution enables you to fine-tune your data easily, saving money and time and helping you generate more revenue.
    • Easy implementation of web services: Simply drag and drop your data sets and algorithms, and link them together to implement web services. It only requires one click to create and publish the web service, which can be used from any device by passing valid credentials.
    • Great documentation: Microsoft Azure provides full stacks of documentation, such as tutorials, quick starts, references, and many other resources that help you understand how to easily build, manage, deploy, and access machine learning solutions effectively.

    Microsoft Azure Machine Learning Benefits:

    • It is fully integrated with Python and R SDKs.
    • It has an updated drag-and-drop interface, generally known as Azure Machine Learning Designer.
    • It supports MLPipelines, where you can build flexible and modular pipelines to automate workflows.
    • It supports multiple model formats depending upon the job type.
    • It has automated model training and hyperparameter tuning with code-first and no-code options.
    • It supports data labeling projects.

    Reviews from Real Users:

    "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.” - Channing S.l, Owner at Channing Stowell Associates

    "The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.” - Chris P., Tech Lead at a tech services company

    "The UI is very user-friendly and the AI is easy to use.” - Mikayil B., CRM Consultant at a computer software company

    "The solution is very fast and simple for a data science solution.” - Omar A., Big Data & Cloud Manager at a tech services company

    TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

    Offer
    Learn more about Microsoft Azure Machine Learning Studio
    Learn more about TensorFlow
    Sample Customers
    Walgreens Boots Alliance, Schneider Electric, BP
    Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
    Top Industries
    REVIEWERS
    Financial Services Firm14%
    Recruiting/Hr Firm14%
    Computer Software Company14%
    Energy/Utilities Company14%
    VISITORS READING REVIEWS
    Computer Software Company24%
    Comms Service Provider19%
    Financial Services Firm6%
    Manufacturing Company6%
    VISITORS READING REVIEWS
    Comms Service Provider27%
    Manufacturing Company23%
    Computer Software Company17%
    Government4%
    Company Size
    REVIEWERS
    Small Business29%
    Midsize Enterprise14%
    Large Enterprise57%
    REVIEWERS
    Small Business63%
    Midsize Enterprise25%
    Large Enterprise13%
    Find out what your peers are saying about Microsoft Azure Machine Learning Studio vs. TensorFlow and other solutions. Updated: January 2022.
    563,780 professionals have used our research since 2012.

    Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 16 reviews while TensorFlow is ranked 2nd in AI Development Platforms with 10 reviews. Microsoft Azure Machine Learning Studio is rated 7.6, while TensorFlow is rated 9.2. The top reviewer of Microsoft Azure Machine Learning Studio writes "Has the ability to do templating and transfer it so that we can do multiple types of models and data mining". On the other hand, the top reviewer of TensorFlow writes "The generator saves us a lot of time and memory in terms of development and the learning process of models". Microsoft Azure Machine Learning Studio is most compared with Databricks, Dataiku Data Science Studio, IBM Watson Studio, Alteryx and H2O.ai, whereas TensorFlow is most compared with OpenVINO, IBM Watson Machine Learning, Wit.ai, Infosys Nia and Caffe. See our Microsoft Azure Machine Learning Studio vs. TensorFlow report.

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