Amazon Comprehend vs. TensorFlow

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Comprehend
Score 7.4 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
TensorFlow
Score 8.3 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
Pricing
Amazon ComprehendTensorFlow
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 ComprehendTensorFlow
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 ComprehendTensorFlow
Top Pros

No answers on this topic

Top Cons
Best Alternatives
Amazon ComprehendTensorFlow
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Posit
Posit
Score 9.2 out of 10
Posit
Posit
Score 9.2 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon ComprehendTensorFlow
Likelihood to Recommend
10.0
(1 ratings)
6.6
(15 ratings)
Usability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
-
(0 ratings)
9.1
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Amazon ComprehendTensorFlow
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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Open Source
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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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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Open Source
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
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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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Open Source
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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Usability
Amazon AWS
No answers on this topic
Open Source
Support of multiple components and ease of development.
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Support Rating
Amazon AWS
No answers on this topic
Open Source
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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Implementation Rating
Amazon AWS
No answers on this topic
Open Source
Use of cloud for better execution power is recommended.
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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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Open Source
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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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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Open Source
  • Learning is s bit difficult takes lot of time.
  • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
  • Once you have learned this, it make your job very easy of getting the good result.
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