What is baikal?
It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines.
baikal is a tool in the Development & Training Tools category of a tech stack.
Key Features
Build non-linear pipelines effortlesslyHandle multiple inputs and outputsAdd steps that operate on targets as part of the pipelineNest pipelinesUse prediction probabilities (or any other kind of output) as inputs to other steps in the pipelineQuery intermediate outputs, easing debuggingFreeze steps that do not require fittingDefine and add custom steps easilyPlot pipelines
baikal Pros & Cons
Pros of baikal
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Cons of baikal
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baikal Alternatives & Comparisons
What are some alternatives to baikal?
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.
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.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
