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11 Ratings
12 Ratings
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Score 7.7 out of 101
11 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow'>trScore algorithm: Learn more.</a>
Score 8.6 out of 101

Likelihood to Recommend

Amazon SageMaker

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.
Thomas Young profile photo

Keras

I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.
Gaurav Yadav profile photo

Pros

Amazon SageMaker

  • 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.
Gavin Hackeling profile photo

Keras

  • Performs well when you are doing some implementation which requires neural network implementation and some other deep learning models
  • It has lots of inbuilt tools which you can have clean your data before processing
  • It supports Tensorflow as its backend, so it can easily use GPU
Rounak Jangir profile photo

Cons

Amazon SageMaker

  • I think that although the algorithms are there and you are using one click, there could be more detailed descriptions located in places so that other users are able to easily find the right formula and tools.
  • Mobile friendly options would be a huge plus, even tracking what employees are using this tool for in regards to reporting.
No photo available

Keras

  • As it is a kind of wrapper library it won't allow you to modify everything of its backend
  • Unlike other deep learning libraries, it lacks a pre-defined trained model to use
  • Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs
Rounak Jangir profile photo

Alternatives Considered

Amazon SageMaker

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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Keras

Keras is a good point where you can learn lots of things and also have hands-on experience. There is not much comparison of Keras with Tensorlow, as Keras is a wrapper library which supports TensorFlow and Theano as backends for computation. But once you have enough knowledge of deep learning and machine learning, then it's better if you use TensorFlow itself or some other library.
Rounak Jangir profile photo

Return on Investment

Amazon SageMaker

  • 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.
Gavin Hackeling profile photo

Keras

  • It helped me in learning the basic concept of deep learning by having hands-on experience.
  • It has helped us to implement our NN with very little time.
  • It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow
Rounak Jangir profile photo

Pricing Details

Amazon SageMaker

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Keras

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

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