ktrain: A Low-Code Library for Augmented Machine Learning
This library addresses the need for easier machine learning application for both beginners and experienced practitioners, though it is incremental as a wrapper around existing tools.
The authors tackled the problem of making machine learning more accessible by developing ktrain, a low-code Python library that simplifies building, training, and applying state-of-the-art models across text, vision, graph, and tabular data, enabling users to solve tasks in as few as three or four lines of code.
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.