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Amazon Tensor FLowWe use Amazon Tensor Flow mainly for classification, regression, and clustering when using large databases and for overseas clients. The cloud capabilities allow us to smoothly provide a full service for our clients overseas,Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses. Tensor Flow allows us to train our models much faster than in our on-premise equipment. Most of the pre-trained models are easy to adapt to our clients' needs.,SageMaker isn't available in all regions. This is complicated for some clients overseas. For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default. Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.,9,Positive: It has allowed us to work with our overseas teams without any large hardware investing. Positive: Pre-trained models significantly reduce the time to develop solutions for our clients. Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.,Microsoft Azure Machine Learning Workbench, Google Cloud AI and Watson Studio (formerly IBM Data Science Experience),Microsoft Azure Machine Learning Workbench
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Amazon Tensor Flow
4 Ratings
Score 8.2 out of 101
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Amazon Tensor Flow Reviews

Amazon Tensor Flow
4 Ratings
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Score 8.2 out of 101
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Mauricio Quiroga-Pascal Ortega profile photo
January 17, 2019

User Review: "Amazon Tensor FLow"

Score 9 out of 10
Vetted Review
Verified User
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We use Amazon Tensor Flow mainly for classification, regression, and clustering when using large databases and for overseas clients. The cloud capabilities allow us to smoothly provide a full service for our clients overseas
  • Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
  • Tensor Flow allows us to train our models much faster than in our on-premise equipment.
  • Most of the pre-trained models are easy to adapt to our clients' needs.
  • SageMaker isn't available in all regions. This is complicated for some clients overseas.
  • For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
  • Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.
A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training.

AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.
Read Mauricio Quiroga-Pascal Ortega's full review

Amazon Tensor Flow Scorecard Summary

About Amazon Tensor Flow

Amazon TensorFlow enables developers to quickly and easily get started with deep learning in the cloud.
Categories:  Machine Learning

Amazon Tensor Flow Technical Details

Operating Systems: Unspecified
Mobile Application:No