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Amazon Web Services (AWS)

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18,253 reviews
  • 447 profiles
  • 222 categories
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4.4
#1 in 34 categories
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Serving customers since
2006
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ELECTRA-Base++

0 reviews

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on WikiPedia and BookCorpus returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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MobileNet V1 0.25 224

0 reviews

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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BERT Large Uncased

0 reviews

It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and then fine-tuning the entire model on [QNLI](https://rajpurkar.github.io/SQuAD-explorer/ ) dataset. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

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BERT Base MEDLINE/PubMed

0 reviews

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on MEDLINE/PubMed returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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MobileNet V1 0.50 128

0 reviews

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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MobileNet V2

0 reviews

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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BERT Large Cased Whole Word Masking SQuAD

0 reviews

It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

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BERT Base Wikipedia and BooksCorpus

0 reviews

It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on WikiPedia and BookCorpus returns an embedding of the input pair of sentences. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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EfficientNet B7

0 reviews

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

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Amazon Web Services (AWS) Reviews

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Profile Name
Star Rating
11953
5318
731
146
96
SG
Sujata G.
Graphic Designer at hous
09/20/2026
Validated Reviewer
Verified Current User
Review source: Organic

Serverless Simplicity with Auto-Scaling and Pay-Per-Use Efficiency

What I like most about AWS Lambda is that I don’t have to worry about managing servers or infrastructure. I can just focus on writing and deploying my code, and Lambda automatically handles the scaling based on demand. It’s also convenient for building event-driven applications, and I like that I only pay for the compute time my functions actually use.
Verified User in Retail
AR
Verified User in Retail
09/16/2026
Validated Reviewer
Review source: Organic

Reliable and scalable compute infrastructure

Amazon EC2 is helpful because it provides flexible, scalable compute capacity without needing to manage physical servers. I like the ability to quickly provision and resize instances based on workload requirements, integrate with other AWS services and support high availability and automation. It also provides with range of instance types, making it easy to optimize performance and cost.
rupali j.
RJ
rupali j.
--CA Finalist ll Audit Associate @crowe II Ex- BDO Rise Pvt Ltd
09/16/2026
Validated Reviewer
Review source: G2 invite
Incentivized Review
Translated Using AI

Easy, Scalable Text Insights with Amazon Comprehend

What I like most about Amazon Comprehend is how easy it is to extract insights from large amounts of text without needing complex machine learning expertise. It saves time, scales efficiently, and helps automate tasks such as sentiment analysis, entity recognition, and document classification with reliable results.

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What is Amazon Web Services (AWS)?

Amazon Web Services (AWS), a subsidiary of Amazon, is a leading cloud computing platform that provides a wide range of on-demand services such as computing power, data storage, databases, networking, and artificial intelligence tools. It enables businesses to build, deploy, and scale applications without investing in physical infrastructure, using a flexible pay-as-you-go pricing model. With a global network of data centers, AWS supports organizations of all sizes—from startups to large enterprises—by offering reliable, secure, and highly scalable solutions for modern digital operations.

Details

Year Founded
2006
Ownership
NASDAQ: AMZN
Website
aws.amazon.com