AutoMLPipeline vs Gradio
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification. | It allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs. |
Pipeline API that allows high-level description of processing workflow;
Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc;
Symbolic pipeline parsing for easy expression of complexed pipeline structures;
Easily extensible architecture by overloading just two main interfaces: fit! and transform!;
Meta-ensembles that allow composition of ensembles of ensembles (recursively if needed) for robust prediction routines;
Categorical and numerical feature selectors for specialized preprocessing routines based on types | Customizable Components; Multiple Inputs and Outputs; Sharing Interfaces Publicly & Privacy |
Statistics | |
GitHub Stars 368 | GitHub Stars 40.4K |
GitHub Forks 28 | GitHub Forks 3.1K |
Stacks 0 | Stacks 37 |
Followers 7 | Followers 24 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to AutoMLPipeline, Gradio?
TensorFlow
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
