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Amazon Comprehend vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon Comprehend
Ranking in Data Science Platforms
19th
Average Rating
8.0
Reviews Sentiment
7.5
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in Data Science Platforms
4th
Average Rating
7.6
Reviews Sentiment
7.0
Number of Reviews
60
Ranking in other categories
AI Development Platforms (3rd)
 

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Amazon Comprehend is 0.5%, down from 0.9% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 5.7%, down from 10.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Pavan Nanjundappa - PeerSpot reviewer
Good job extracting entities and does the classification in the healthcare products
I would like to see a feature that allows me to create or generate a summary based on the extracted entities. Amazon Comprehend works with a large pool of doctors. They're building the product based on working with domain experts. I don't have any suggestions except if they can provide a feature for generating the summary for the entities that has been updated.
HéctorGiorgiutti - PeerSpot reviewer
Requires minimal maintenance, is scalable, and stable
The initial setup depends on the developer's knowledge of machine learning models as to whether it is easy or difficult. With a good understanding of these models, then we can get to work quickly in the environment. With 20 years of experience in IT, making applications on legacy platforms and non-web platforms, I have found that Azure has a very good implementation. The platform is so comprehensive that it doesn't matter what kind of work we do, we can find the tools needed to get the job done. In comparison to what I was doing five years ago, Azure is a great platform and I really enjoy working with it. We should allocate up to 12 percent of our project time to deployment. Depending on the complexity of the solution, we should expect to spend more time on deployment.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Amazon Comprehend works with a large pool of doctors. They're building the product based on working with domain experts."
"The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server."
"The product's initial setup phase is easy."
"The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server."
"It's a great option if you are fairly new and don't want to write too much code."
"I've developed a couple of chatbots using Azure OpenAI, leveraging its documented solutions and APIs. The tools available make it straightforward to implement machine learning solutions. However, there are challenges, such as hallucinations and security issues, but overall, it works well and is quite fast, allowing for the development of interesting projects."
"Overall, I rate Microsoft Azure Machine Learning Studio a seven out of ten."
"Visualisation, and the possibility of sharing functions are key features."
"The visualizations are great. It makes it very easy to understand which model is working and why."
 

Cons

"It is a bit complex to scale. It is still evolving as a product."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"They should have a desktop version to work on the platform."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"Microsoft Azure Machine Learning Studio could improve by adding pixel or image analysis. This is a priority for me."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
 

Pricing and Cost Advice

Information not available
"The licensing cost is very cheap. It's less than $50 a month."
"There is a lack of certainty with the solution's pricing."
"The platform's price is low."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
"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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Top Industries

By visitors reading reviews
Financial Services Firm
12%
Manufacturing Company
12%
University
10%
Healthcare Company
8%
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What needs improvement with Amazon Comprehend?
I would like to see a feature that allows me to create or generate a summary based on the extracted entities. Amazon Comprehend works with a large pool of doctors. They're building the product base...
What is your primary use case for Amazon Comprehend?
I use it to extract medical entities for doctors like the deceased and so on. It does the job well for our requirements. It is mainly for automation.
What advice do you have for others considering Amazon Comprehend?
Overall, I would rate the product an eight out of ten. I would recommend Amazon AWS Medical Comprehend for healthcare products because it does a good job extracting entities and does the classifica...
Which do you prefer - Databricks or Azure Machine Learning Studio?
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 ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?
Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one to ten in terms of affordability.
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
 

Overview

 

Sample Customers

LexisNexis, Vibes, FINRA, VidMob
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Databricks, Knime, Amazon Web Services (AWS) and others in Data Science Platforms. Updated: January 2025.
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