This Lightweight, Python library ‘Steppy’ can be used for fast and reproducible data science/machine learning experimentation

‘Steppy’ is a light python-3 library that can be used for fast and reproducible data science/machine learning experimentation. It reduces the burden of data scientists from software development issues. The minimal interface does not impose constraints for ‘Steppy’; instead, it enables clean machine learning pipeline design.

‘Steppy’ solves some of the data science project problems with the help of minimal interface for building machine learning pipelines. It uses two simple abstractions: Step and Tranformer

Step: It is the execution wrapper over the transformer. Example: Checking intermediate results.

Tranformer: It represents the computation step and performs operation on data. Mostly, Transformers are neural networks, machine learning algorithms..

Installation

Steppy requires python3.5 or above.

pip3 install steppy

Getting started with steps (Code Source: https://github.com/neptune-ml/steppy-examples/blob/master/tutorials/1-getting-started.ipynb)

This notebook shows how to create steps, fit them to data, transform new data and take advantage of persistence

%load_ext autoreload
%autoreload 2

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

from steppy.base import Step, BaseTransformer

EXPERIMENT_DIR = './ex1'
import shutil

# By default pipelines will try to load previously trained models so we delete the cache to ba sure we're starting from scratch
shutil.rmtree(EXPERIMENT_DIR, ignore_errors=True)

Github: https://github.com/neptune-ml/steppy

Tutorial notebooks (their repository):

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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