5 Low-code and no code libraries for machine learning engineers
By: Saurav Anand
Tags: Machine Learning, Data Analysis, Data Science, Artificial Intelligence

Smart low code platform make the development lifecycle seamless and anable developers to easily prototype and iterate. :- Dick Wooden

Introduction

Low-code and No-code platforms are going to be the game changers in the software industry. The increasing use of the low-code and no code machine learning libraries makes it easier for the developers to develop projects faster.It promises to make software itself even more ubiquitous than it is today.

Low-Code Versus No-Code

Low-code software is software that can be fully customized with a “low” (minimal) amount of programming. Instead of taking weeks or months to develop a custom application, it can be done in a few hours or days.It is faster and much cheaper than ever before but it still requires coding by someone knowledgeable about programming.

No-code software, on the other hand, takes the low-code concept to another level, allowing literally anyone to tailor an application to their needs without any programming knowledge whatsoever.

Mostly they both comes under the same category of “low level” code. In this blog , we are going to look into these libraries in detail which are really beneficial for the users.

Let’s get started

source : google

1. Pycaret

This is an open source, low-code machine learning (ML) library in Python. The library automates ML workflows. It is an end-to-end ML and model management tool. You can easily tune the hyperparameters of the various models on GPU. Update the deploy_model function and even plot the model function included with scale parameter.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few words only.PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.

Official Website : https://www.pycaret.org

: https://www.pycaret.org Documentation : https://pycaret.readthedocs.io/en/latest/

: https://pycaret.readthedocs.io/en/latest/ Demo Link: https://github.com/pycaret/pycaret-demo-tfmeetup

2.H2O AutoML

This is another automation tool used as the combined interface for multiple models and algorithms. It supports both Python and R programming languages. For beginners, it helps to automate preprocessing, training, validation and fine-tuning models.H2O’s AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

3.Auto-ViML

This is another low-code library known as ‘Auto_ViML’. It accepts any dataset that is in the form of the Pandas data frame. The tool can perform data cleaning and category feature transformation. Auto-ViML provides verbose output to allow for a great deal of understanding and interpretability.

Auto_ViML was designed for building High Performance Interpretable Models with the fewest variables.

4.CreateML

This is a purely no-code drag and drop solution developed by Apple. It works on macOS and comes with a bunch of pre-trained model templates. Before the training, you can set the iteration count and fine-tune the metrics. For models such as style transfer, CreateML provides real-time results on the validation model.

Official Website: https://developer.apple.com/machine-learning/create-ml/

https://developer.apple.com/machine-learning/create-ml/ Documentation :https://developer.apple.com/documentation/createml

:https://developer.apple.com/documentation/createml Demo Link : https://github.com/ShefaliGoel95/CreateML

5. Google Cloud AutoML

Google has created the Apple-like AutoML tool. AutoML by Google Cloud offers various natural language, AutoML translation, and video intelligence products. It also helps developers with less ML expertise to build models specific to their use-case.

Conclusion

The use of these libraries helps us in automating the complex steps of the ML lifecycle along with taking care of the accuracy and quality of the model.The data processing time is reduced and is saved which allows the engineer to concentrate to the other phases . Thus we can say that use of this libraries helps us to to make proper use of machine learning in data science.Accuracy is measured well in machine learning but automated machine learning is one step ahead and fine-tunes the data more effectively and reduces the error rate more precisely.

Happy Reading,

Saurav Anand

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