Compare Amazon Comprehend vs. Microsoft Azure Machine Learning Studio

Cancel
You must select at least 2 products to compare!
Most Helpful Review
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: March 2021.
474,319 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:

Pricing and Cost Advice
Information Not Available
"When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly.""From a developer's perspective, I find the price of this solution high.""The licensing cost is very cheap. It's less than $50 a month."

More Microsoft Azure Machine Learning Studio Pricing and Cost Advice »

report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
474,319 professionals have used our research since 2012.
Questions from the Community
Ask a question

Earn 20 points

Top Answer: It's good for citizen data scientists, but also, other people can use Python or .NET code.
Top Answer: The licensing cost is very cheap. It's less than $50 a month would costs for multiple users.
Top Answer: Every tool requires some improvement. They have already improved many things. They had added new features and a new pipeline. They should have an on-premise version, other than Python and R Studio… more »
Ranking
24th
Views
551
Comparisons
515
Reviews
0
Average Words per Review
0
Rating
N/A
4th
Views
14,561
Comparisons
11,621
Reviews
12
Average Words per Review
559
Rating
7.7
Popular Comparisons
Also Known As
Azure Machine Learning
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.

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 Company19%
Comms Service Provider9%
Healthcare Company7%
VISITORS READING REVIEWS
Computer Software Company27%
Comms Service Provider19%
Energy/Utilities Company6%
Manufacturing Company6%
Company Size
No Data Available
REVIEWERS
Small Business40%
Midsize Enterprise7%
Large Enterprise53%
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: March 2021.
474,319 professionals have used our research since 2012.

Amazon Comprehend is ranked 24th in Data Science Platforms while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 12 reviews. Amazon Comprehend is rated 0.0, while Microsoft Azure Machine Learning Studio is rated 7.6. On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Amazon Comprehend is most compared with Amazon SageMaker and IBM Watson Studio, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx, IBM Watson Studio, Amazon SageMaker and Anaconda.

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.