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GraphPipe vs scikit-learn: What are the differences?
What is GraphPipe? Machine Learning Model Deployment Made Simple, by Oracle. GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.
What is scikit-learn? Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
GraphPipe and scikit-learn can be primarily classified as "Machine Learning" tools.
GraphPipe and scikit-learn are both open source tools. It seems that scikit-learn with 36K GitHub stars and 17.6K forks on GitHub has more adoption than GraphPipe with 643 GitHub stars and 91 GitHub forks.
A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.
Pros of GraphPipe
Pros of scikit-learn
- Scientific computing25
- Easy19
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Cons of GraphPipe
Cons of scikit-learn
- Limited2
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What is GraphPipe?
What is scikit-learn?
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