Amazon Comprehend vs IBM Watson Studio comparison

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Amazon Web Services (AWS) Logo
1,153 views|955 comparisons
IBM Logo
3,410 views|2,249 comparisons
100% willing to recommend
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Executive Summary

We performed a comparison between Amazon Comprehend and IBM Watson Studio based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: April 2024).
768,246 professionals have used our research since 2012.
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Pricing and Cost Advice
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  • "Watson Studio's pricing is reasonable for what you get."
  • "IBM Watson Studio is a reasonably priced product"
  • "IBM Watson Studio is an expensive solution."
  • "The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."
  • More IBM Watson Studio Pricing and Cost Advice →

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    Top Answer:From an improvement perspective, I would say that if the deployment environment and IBM Watson Studio's environment are separated, then it would be good. I would like them to be one and offer users a… more »
    Ranking
    21st
    Views
    1,153
    Comparisons
    955
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    11th
    Views
    3,410
    Comparisons
    2,249
    Reviews
    5
    Average Words per Review
    426
    Rating
    8.2
    Comparisons
    Also Known As
    Watson Studio, IBM Data Science Experience, Data Science Experience, DSx
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    IBM
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    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.

    IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

    Sample Customers
    LexisNexis, Vibes, FINRA, VidMob
    GroupM, Accenture, Fifth Third Bank
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company13%
    Financial Services Firm10%
    Educational Organization8%
    Manufacturing Company7%
    REVIEWERS
    Manufacturing Company22%
    Insurance Company11%
    Marketing Services Firm11%
    Comms Service Provider11%
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Computer Software Company11%
    Comms Service Provider8%
    Educational Organization7%
    Company Size
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise13%
    Large Enterprise71%
    REVIEWERS
    Small Business71%
    Large Enterprise29%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise12%
    Large Enterprise67%
    Buyer's Guide
    Data Science Platforms
    April 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: April 2024.
    768,246 professionals have used our research since 2012.

    Amazon Comprehend is ranked 21st in Data Science Platforms while IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews. Amazon Comprehend is rated 0.0, while IBM Watson Studio is rated 8.2. On the other hand, the top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". Amazon Comprehend is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio and Google Cloud Datalab, whereas IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI, Google Vertex AI and IBM SPSS Modeler.

    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.