Anaconda vs Microsoft Azure Machine Learning Studio comparison

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Executive Summary

We performed a comparison between Anaconda and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Alteryx, Microsoft and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: November 2022).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager, "Conda", makes life easy when it comes to managing and installing packages."

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"It's good for citizen data scientists, but also, other people can use Python or .NET code.""Their support is helpful.""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 solution is very easy to use, so far as our data scientists are concerned.""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 most valuable feature is its compatibility with Tensorflow.""The initial setup is very simple and straightforward.""Their web interface is good."

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Cons
"Anaconda should be optimized for RAM consumption."

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"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.""In the future, I would like to see more AI consultation like image and video classification, and improvement in the presentation of data.""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.""It would be nice if the product offered more accessibility in general.""They should have a desktop version to work on the platform.""Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me.""n the solution, there is the concept of workspaces, and there is no means to share the computing infrastructure across those workspaces.""Technical support could improve their turnaround time."

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Pricing and Cost Advice
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  • "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."
  • "In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
  • "My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
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    Questions from the Community
    Top Answer:With Anaconda Navigator, we have been able to use multiple IDEs such as JupyterLab, Jupyter Notebook, Spyder, Visual Studio Code, and RStudio in one place. The platform-agnostic package manager… more »
    Top Answer:The solution's support is important and needs to be better. I don't have the last update due to the fact that when I tried to update it I had an error and ran into issues. It's not just me; lots of… more »
    Top Answer:In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing… more »
    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.
    Ranking
    10th
    Views
    6,008
    Comparisons
    4,734
    Reviews
    1
    Average Words per Review
    151
    Rating
    9.0
    3rd
    Views
    22,171
    Comparisons
    18,535
    Reviews
    14
    Average Words per Review
    507
    Rating
    7.6
    Comparisons
    Also Known As
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

    Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

    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

    Offer
    Learn more about Anaconda
    Learn more about Microsoft Azure Machine Learning Studio
    Sample Customers
    LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    REVIEWERS
    Financial Services Firm20%
    Manufacturing Company20%
    Pharma/Biotech Company10%
    Computer Software Company10%
    VISITORS READING REVIEWS
    Computer Software Company18%
    Financial Services Firm15%
    Comms Service Provider11%
    Government9%
    REVIEWERS
    Financial Services Firm15%
    Media Company8%
    Comms Service Provider8%
    Retailer8%
    VISITORS READING REVIEWS
    Computer Software Company15%
    Comms Service Provider10%
    Financial Services Firm10%
    Manufacturing Company7%
    Company Size
    REVIEWERS
    Small Business44%
    Large Enterprise56%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise11%
    Large Enterprise71%
    REVIEWERS
    Small Business28%
    Midsize Enterprise10%
    Large Enterprise62%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise13%
    Large Enterprise71%
    Buyer's Guide
    Data Science Platforms
    November 2022
    Find out what your peers are saying about Databricks, Alteryx, Microsoft and others in Data Science Platforms. Updated: November 2022.
    656,862 professionals have used our research since 2012.

    Anaconda is ranked 10th in Data Science Platforms with 1 review while Microsoft Azure Machine Learning Studio is ranked 3rd in Data Science Platforms with 15 reviews. Anaconda is rated 9.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Anaconda writes "Supported by multiple IDEs, easy to install and manage packages". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Has a drag and drop feature and easier learning curve, but the number of algorithms available could still be improved". Anaconda is most compared with Amazon SageMaker, Databricks, Cloudera Data Science Workbench, SAP Analytics Cloud and Microsoft BI, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Amazon SageMaker, TensorFlow, Dataiku Data Science Studio and Google Cloud AI Platform.

    See our list of best Data Science Platforms vendors.

    We monitor all Data Science 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.