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 9.0 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.
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Pricing
Amazon Comprehend
Amazon 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
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Offerings
Pricing Offerings
Amazon Comprehend
Amazon SageMaker
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon Comprehend
Amazon SageMaker
Considered Both Products
Amazon Comprehend
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Chose Amazon Comprehend
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 …
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 …
Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as …
We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their …
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.
Amazon Sagemaker suits well in areas of data science and Machine learnings where medium to high-volume data is to be used for analysis. For a lean and platform agnostic deployment, it provides kubernetes integration to containerize the solution and deploy on any platform. It is one of the best solution for technical users for training Machine Learning models.
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.
SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.
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.
We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.
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.
