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Amazon Comprehend vs Microsoft Azure Machine Learning Studio comparison

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July 2022
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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."
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    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
    20th
    Views
    1,107
    Comparisons
    1,010
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    Average Words per Review
    0
    Rating
    N/A
    3rd
    Views
    19,501
    Comparisons
    16,161
    Reviews
    14
    Average Words per Review
    461
    Rating
    7.9
    Comparisons
    Also Known As
    Azure Machine Learning, MS Azure Machine Learning Studio
    Learn More
    Overview

    Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required.

    There is a treasure trove of potential sitting in your unstructured data. Customer emails, support tickets, product reviews, social media, even advertising copy represents insights into customer sentiment that can be put to work for your business. The question is how to get at it? As it turns out, Machine learning is particularly good at accurately identifying specific items of interest inside vast swathes of text (such as finding company names in analyst reports), and can learn the sentiment hidden inside language (identifying negative reviews, or positive customer interactions with customer service agents), at almost limitless scale.

    Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic. You can also use AutoML capabilities in Amazon Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your organization’s needs.

    For extracting complex medical information from unstructured text, you can use Amazon Comprehend Medical. The service can identify medical information, such as medical conditions, medications, dosages, strengths, and frequencies from a variety of sources like doctor’s notes, clinical trial reports, and patient health records. Amazon Comprehend Medical also identifies the relationship among the extracted medication and test, treatment and procedure information for easier analysis. For example, the service identifies a particular dosage, strength, and frequency related to a specific medication from unstructured clinical notes.

    Amazon Comprehend is fully managed, so there are no servers to provision, and no machine learning models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.

    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 Amazon Comprehend
    Learn more about Microsoft Azure Machine Learning Studio
    Sample Customers
    LexisNexis, Vibes, FINRA, VidMob
    Walgreens Boots Alliance, Schneider Electric, BP
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company22%
    Media Company13%
    Comms Service Provider11%
    Insurance Company8%
    REVIEWERS
    Financial Services Firm17%
    Recruiting/Hr Firm8%
    Computer Software Company8%
    Energy/Utilities Company8%
    VISITORS READING REVIEWS
    Computer Software Company21%
    Comms Service Provider16%
    Financial Services Firm7%
    Manufacturing Company7%
    Company Size
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise6%
    Large Enterprise81%
    REVIEWERS
    Small Business26%
    Midsize Enterprise11%
    Large Enterprise63%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise14%
    Large Enterprise69%
    Buyer's Guide
    Data Science Platforms
    July 2022
    Find out what your peers are saying about Databricks, Alteryx, Microsoft and others in Data Science Platforms. Updated: July 2022.
    620,068 professionals have used our research since 2012.

    Amazon Comprehend is ranked 20th in Data Science Platforms while Microsoft Azure Machine Learning Studio is ranked 3rd in Data Science Platforms with 15 reviews. Amazon Comprehend is rated 0.0, while Microsoft Azure Machine Learning Studio is rated 7.8. On the other hand, 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". Amazon Comprehend is most compared with Amazon SageMaker, Google Cloud Datalab and IBM Watson Studio, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio and IBM Watson Studio.

    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.