Keras vs. The Kepler Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Keras
Score 7.0 out of 10
N/A
Keras is a Python deep learning libraryN/A
The Kepler Platform
Score 10.0 out of 10
Mid-Size Companies (51-1,000 employees)
Advanced Self-Serve AI and AutoML Platform. No Machine Learning (ML) Experience Required. Kepler is a SaaS AI business platform that aims to redefine how enterprises work with data-driven intelligence. The Kepler platform puts AI in the hands of any business user, extending advanced data-driven decision-making across the organization to increase revenue, reduce cost and mitigate risk. The vendor states that through the use of automated data science…N/A
Pricing
KerasThe Kepler Platform
Editions & Modules
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Offerings
Pricing Offerings
KerasThe Kepler Platform
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsComputing/storage units are included in the price.
More Pricing Information
Community Pulse
KerasThe Kepler Platform
Considered Both Products
Keras
Chose Keras
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
Chose Keras
For beginners, I always recommend starting with Keras, because it's really easy to use and learn at first. There is not much pre-requisite for this to start with.
Chose Keras
Keras is much easier to learn as compared to TensorFlow. It also has a lot of built-in functionality that makes it much better than the alternatives.
Chose 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 …
Chose Keras
Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer …
Chose Keras
TensorFlow and Caffe are bit hard to learn but they give you power to implement everything by you own. But most of the time it is not required to implement our own algorithm, we can solve the problem with just using the already provided algorithms. As compared to TensorFlow and …
The Kepler Platform

No answer on this topic

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User Ratings
KerasThe Kepler Platform
Likelihood to Recommend
8.1
(0 ratings)
-
(0 ratings)
Usability
7.7
(0 ratings)
-
(0 ratings)
Support Rating
8.2
(0 ratings)
-
(0 ratings)
User Testimonials
KerasThe Kepler Platform
Likelihood to Recommend
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.
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Pros
  • Implementing neural networks and deep learning models is easy with this.
  • Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
  • It has good integration with other devices like Android.
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Cons
  • I didn't face any issue so far.
  • The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.
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Usability
The reason for giving this much rating. 1. It makes my job really easy and fast. 2. Strong community support. 3. Overall cost.
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Support Rating
Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
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Alternatives Considered
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
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Return on Investment
  • 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
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ScreenShots

The Kepler Platform Screenshots

Screenshot of Guided model training configurationScreenshot of Automated model training processScreenshot of Kepler platform workflow evaluation dashboardScreenshot of Time Series Forecasting chartScreenshot of Global feature impact chartScreenshot of Automated Data Science Workflow Selection