Appen vs. Pytorch

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Appen
Score 7.1 out of 10
N/A
The Appen platform combines human intelligence from over one million people all over the world with models to create training data for ML projects. Appen users can upload data to the Appen platform, and they provide the annotations, judgments, and labels needed to help create ground truth for models.N/A
Pytorch
Score 9.4 out of 10
N/A
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A
Pricing
AppenPytorch
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
AppenPytorch
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Best Alternatives
AppenPytorch
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
AppenPytorch
Likelihood to Recommend
10.0
(1 ratings)
9.4
(5 ratings)
User Testimonials
AppenPytorch
Likelihood to Recommend
Appen
It is well suited for the users and potential employee who are free of any job perspective and need their free time to be utilized. Users can use their free time to be used for submission of interesting tasks.
Whereas the number of tasks are very less and processing time is also very extensive and recruitment takes time more.
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Open Source
They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
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Pros
Appen
  • Project listing
  • Hiring of the potential and qualified users
  • Tracking of the projects
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Open Source
  • Provides Benchmark datasets to test your custom algorithm
  • Provides with a lot of pre-coded neural net components to use for your flow
  • Gives a framework to write really abstract code.
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Cons
Appen
  • Selection procedure is bit .
  • The questionnaire need to be reviewed.
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Open Source
  • Distributed data parallel still seems to be complicated
  • Support for easy deployment to servers
  • Torchvision to have support for latest models with pertained weights
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Alternatives Considered
Appen
Appen offers projects mostly related to my native language and also according to my expertise . It offers very interesting projects to be completed , which requires not very expertise and less time to be completed for each task. It is also very convenient to use after selection for the task and also well rewarding against the time consumed for the task completion.
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Open Source
As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks. You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.
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Return on Investment
Appen
  • It has Positive impact as it provides opportunity for new jobs in my area of expertise.
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Open Source
  • I'd estimate I can build a model 50% faster on pytorch vs other frameworks
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