Amazon Comprehend vs. Amazon SageMaker

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Comprehend
Score 7.9 out of 10
N/A
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend uses machine learning to help uncover insights and relationships in 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…
$0
per unit
Amazon SageMaker
Score 8.2 out of 10
N/A
Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.N/A
Pricing
Amazon ComprehendAmazon SageMaker
Editions & Modules
Syntax Analysis
$0.00005
per unit
Key Phrase Extraction
$0.0001
per unit
Sentiment Analysis
$0.0001
per unit
Entity Recognition
$0.0001
per unit
Language Detection
$0.0001
per unit
Pll Detection
$0.0001
per unit
Event Detection Per Event Type
$0.003
per unit
No answers on this topic
Offerings
Pricing Offerings
Amazon ComprehendAmazon SageMaker
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon ComprehendAmazon SageMaker
Top Pros

No answers on this topic

Top Cons
Best Alternatives
Amazon ComprehendAmazon SageMaker
Small Businesses
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
Medium-sized Companies
Posit
Posit
Score 9.0 out of 10
Google Cloud AI
Google Cloud AI
Score 8.3 out of 10
Enterprises
Dataiku
Dataiku
Score 8.6 out of 10
Dataiku
Dataiku
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon ComprehendAmazon SageMaker
Likelihood to Recommend
10.0
(1 ratings)
9.0
(6 ratings)
User Testimonials
Amazon ComprehendAmazon SageMaker
Likelihood to Recommend
Amazon AWS
Specifically, it starts processing millions of documents in minutes by leveraging the power of machine learning without having trained models from scratch. If any of the content contains personally identifiable information not only can Amazon Comprehend locate it but it will also redact or mask it. Using NLP techniques Amazon Comprehend goes well beyond keyword search or rules-based tagging to accurately classify documents. For my task or development, I cannot find any difficulties with Amazon Comprehend.
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Amazon AWS
Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.
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Pros
Amazon AWS
  • Amazon Comprehend identifies the language of the text and extracts Key-phrases, places, people, brands or events.
  • It can build a custom set of entities or text classification models that are tailored uniquely to the organisation's need
  • Amazon Comprehend's medical can be used to identify medical conditions, medications, dosages, strength and frequencies from sources like doctor's notes, clinical trial reports and patient health records. This service is very good and with well an accuracy or confidence score.
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Amazon AWS
  • Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
  • Customization and easy to alter and change.
  • If you already are an Amazon user, you do not need to transition over to another software.
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Cons
Amazon AWS
  • It will be great if Amazon Comprehend provide support specifically for litigation or related text documents to extract insights from it.
  • For REST API support using JAVA SDK, it will be great for developers if they provide support for testing without any credentials or account details.
  • Setting up for REST API integration can be as simple as possible.
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Amazon AWS
  • The UI can be eased up a bit for use by business analysts and non technical users
  • For huge amount of data pull from legacy solutions, the platform lags a bit
  • Considering ML is an emerging topic and would be used by most of the organizations in future, the pipeline integrations can be optimized
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Alternatives Considered
Amazon AWS
For natural language processing tasks or techniques, there are many service providers out there in the market such as Azure Cloud Services, IBM Watson and Google Cloud Platform (GCP), but compared with them, Amazon Comprehend is the best service provider in contents of accuracy, speed of processing multilingual text, supporting SDK for most of the languages and well documented.
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Amazon AWS
Amazon SageMaker comes with other supportive services like S3, SQS, and a vast variety of servers on EC2. It's very comfortable to manage the process and also support the end application by one click hosting option. Also, it charges on the base of what you use and how long you use it, so it becomes less costly compared to others.
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Return on Investment
Amazon AWS
  • It supports better and accurately as compared with our existing or old implementations. So, we fulfil our needs as per clients' requirements and it will help to grow or improve client satisfaction.
  • For these specific requirements, we do not require any machine learning engineers or related professionals to hire in our organisation.
  • None of any negative sides can be affected our business or distract existing clients.
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Amazon AWS
  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
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ScreenShots