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PredictionIO vs TensorFlow: What are the differences?
Developers describe PredictionIO as "Open Source Machine Learning Server". PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PredictionIO and TensorFlow can be primarily classified as "Machine Learning" tools.
"Predict Future" is the primary reason why developers consider PredictionIO over the competitors, whereas "High Performance" was stated as the key factor in picking TensorFlow.
PredictionIO is an open source tool with 11.8K GitHub stars and 1.92K GitHub forks. Here's a link to PredictionIO's open source repository on GitHub.
Uber Technologies, 9GAG, and StyleShare Inc. are some of the popular companies that use TensorFlow, whereas PredictionIO is used by 500 Startups, Betaout, and Tokopedia. TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to PredictionIO, which is listed in 5 company stacks and 5 developer stacks.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Pros of PredictionIO
- Predict Future8
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of PredictionIO
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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What is TensorFlow?
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