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TensorFlow vs Continuous Machine Learning: What are the differences?
Developers describe TensorFlow 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. On the other hand, Continuous Machine Learning is detailed as "CI/CD for Machine Learning Projects". Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.
TensorFlow and Continuous Machine Learning can be primarily classified as "Machine Learning" tools.
TensorFlow is an open source tool with 146K GitHub stars and 82K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.
Pros of Continuous Machine Learning
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 Continuous Machine Learning
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2















