These Machine Learning Libraries save 80% of your time

These libraries will save you most of the work in Machine Learning and this… without reducing the quality of your model !

In Machine Learning, many studies, like the one conducted by Forbes, have shown that it is not the creation of the model that takes the most time.

Instead, it is the analysis, preparation and cleaning of the data that takes up the most time in the work of a Machine Learning Engineer.

These steps are all part of what we call Preprocessing.

What if I told you that solutions exist to avoid this tedious work ?

Let’s see it right away with these 3 Python AutoML libraries : Automatic Machine Learning !

TPOT

The first library we’d like to introduce is TPOT (pronounced “tea pot”).

The developers behind TPOT have designed this library as a Data Science Assistant. It allows you to optimize your Machine Learning pipelines by using Genetic algorithms (a type of algorithm currently used by Machine Learning researchers).

You will have no trouble getting familiar with TPOT since it is based on Sckit-Learn, the number one library for traditional Machine Learning.

With this library you just have to load your dataset and let the TPOT algorithm take care of the hard work !

AutoGluon

est la deuxième librairie que l’on souhaite vous présenter. Les développeurs de ce package propose de résoudre 4 objectifs :

• Implémenter des modèles de ML et DL capable de traites des données brutes
• Permettre a tous les Data Scientists d’utiliser les algorithmes à la pointe de la recherche
• Optimiser automatiquement les hyperparamètres de votre modèle pour qu’il s’adapte facilement à la structure de vos données
• Personnalisez vos modèle et vos pipelines à votre manière pour exploiter le meilleur d’AutoGluon

En plus de cela, des tutoriels pour utiliser facilement leur librairie sont disponible directement sur leur site. AutoGluon est peu connu mais très prometteur !

AutoGluon is the second library that we would like to present you. The developers of this package propose to achieve 4 goals :

• Implement ML and DL models capable of processing raw data
• Allow all Data Scientists to use state-of-the-art algorithms
• Automatically optimize your model’s hyperparameters to easily fit your data structure
• Customize your models and pipelines to get the most out of AutoGluon

In addition to that, tutorials to easily use their library are available directly on their website. AutoGluon is not well known but very promising !

Auto-Keras

La dernière librairie qu’on souhaitait vous présenter et que vous connaissez peut-être déjà s’appelle

D’après François Chollet, c’est LA librairie sur laquelle vont se concentrer les développeurs de Keras dans les prochaines années. Effectivement, il considère que l’AutoML va prendre une place majeure dans les années qui arrivent. Il s’est fixé un objectif :

The last but not least library that we wanted to introduce you and that you may already know is called Auto-Keras.

According to François Chollet, it is THE library on which the Keras developers will concentrate in the next few years. Indeed, he considers that AutoML will take a major place in the years to come. He has set himself a goal :

As a Tool Maker, we are trying to make the future happened faster.

François Chollet, Keras: The Next Five Years, 2020

This is a library that we have already discussed in this tutorial.

Among few things, AutoKeras allowed me to reach an accuracy of 83% in a Kaggle competition. With Auto-Keras It took only 4 lines of code to build my Machine Learning model.

Things to know

The AutoML libraries will not allow you to learn Data Science in a day.

However, it is a solution that, in many cases, is more efficient than a classical Machine Learning algorithm.

Indeed, AutoML libraries will save you all the preprocessing time of the data while keeping a high quality of result.

That’s why we at Inside Machine Learning think that AutoML is an essential skill to learn for future Artificial Intelligence Engineers.