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Dispatch vs TensorFlow: What are the differences?
Developers describe Dispatch as "API for on-demand marketplaces. Real-time availability, location, response times and more". The Dispatch platform is built to help field service businesses and on-demand marketplaces stay connected with their providers and consumers. It is built from the ground up on SOA (Service-Oriented Architecture) principles. The Dispatch API allows for complete first-class interaction, allowing for deep integrations and cutting-edge applications. 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.
Dispatch belongs to "On-Demand as a Service" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".
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 Dispatch
- Amazing support1
- Tons of integrations1
- Really great mobile app1
- On-Demand Marketplace1
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 Dispatch
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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What is Dispatch?
What is TensorFlow?
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