Top 10 Automatic Machine Learning Frameworks in 2024

AutoML Frameworks

Today we are here with the top automatic machine learning (AutoML) frameworks. AutoML means the automating of machine learning applications to solve practical problems. AutoML impresses businesses with its ocean of opportunities. A business has to do so many things with huge data sets.

Data processing, choice of model, optimization of hyper-parameters, and training- all are part of it. It is a highly time and money-consuming process. In such a scenario, AutoML is the only solution. So, today, we will discuss the top 9 AutoML frameworks for machine learning applications.

Top 10 AutoML Frameworks

There are many frameworks to create automatic machine learning applications. Here we have listed the best automatic machine learning (AutoML) frameworks you should learn. Here we go:

1. ML Box

ML Box is one of the powerful, automated data Python-based AutoML frameworks. The package ML box contains three sub-packages – pre-processing, optimization, and prediction. Pre-processing means reading and pre-processing the data.  ML Box can test and optimize a wide range of data. And also there is a package to predict the target from the test dataset.

Due to all these advanced features, ML Box can provide fast data processing. It offers highly robust feature selection and leak detection along with hyper-parameter optimization. The accuracy level is very high, which places it on the top. You can get state-of-the-art predictive models for classification and regression. Besides this, developers have tested ML Box on Kaggle, and they have got impressive results.

2. Auto-SKLearn

Auto-SKLearn is an automated machine learning software package based on sci-kit-learn. The unique feature of this is, that users don’t need to use all the complex steps of machine learning. You don’t need to do algorithm selection and hyperparameter tuning.

Also, it offers feature engineering methods such as One-Hot, digital feature standardization, and PCA. It creates a pipeline to use Bayes search to optimize the featured channel. Auto-SKLearn is a good performer with medium and small databases.

However, it can’t produce a deep learning framework with large datasets.  The Auto-SKLearn AutoML package offers 14 pre-processed classification algorithms. The accuracy is also fair enough for a machine learner. These qualities keep this in the list of top auto ml frameworks.

3. Tree-Based Pipeline Optimization Tool (TPOT)

TPOT became popular in the year 2018. This year, GitHub listed TPOT in its top 10 AutoML frameworks. Since then, TPOT has been popular among automated machine learners. It is a tree-based pipeline optimization tool to uses generic algorithms.

It allows the users to optimize machine learning pipelines using its classifier methods. Also, it explores numerous possible pipelines to find out the best-fit one for the data. Thus it can automate the most time-consuming part of machine learning with its intelligence. 

After searching, it gives you the Python code of the best pipelines. And most importantly, this TPOT is based on sci-kit-learn. So you can experience similar codes if you are familiar with sci-kit-learn.    

4. H2O AutoML

If you are searching for a deep learning mechanism, H2O is the best solution for you. H2O is also capable of performing many tasks requiring complex coding. H2O offers you statistical and ML algorithms.

Using these, you can easily handle gradient-boosted machines and complex learning systems. It uses its algorithms to create a pipeline. Also, it provides feature engineering methods and model hyper-parameters for optimization.

H2O can automate most of the complex tasks like model validation, model adjustment, model selection, and model deployment. Additionally, it provides automatic visualization and machine learning interpretation. Due to these, H2O is on the list of top AutoML frameworks.

5. Auto Keras

It is an open-source AutoML software library developed by DATA Lab. Also, it is built on network morphism to boost optimization. It aims especially at Bayesian optimization. This framework can search automatically for hyper-parameters and architecture. This will help you in handling complex models.

This framework for AutoML takes the help of neural architecture (NAS) algorithms to conduct searches. It ultimately eliminates the need for deep learning engineers. This framework is also based on the sci-kit-learn API. However, it uses Keras for powerful neural network search. It gives this framework an edge over other sci-kit-based frameworks.

6. Google Cloud AutoML

This is the automated machine learning framework launched by Google. This also provides the integrated power of neural network architecture. It provides a graphical user interface (GUI). It is simple to use.

Beginners can easily operate this framework. It is really useful for data scientists, who have limited knowledge of machine learning. However, this is a paid platform, so you can only use this for commercial projects.

The cost of this depends upon the time you spend on training the models. Also, it charges for the number of sending images. Anyway, you can avail of this auto ML toolkit free for research purposes.

7. TransmogrifAI

It is an AutoML library based on the Apache Spark framework. You can use this for structural data written in Scala. It also allows you to achieve accurate predictions for deep learning models. Meanwhile, it reduces time effectively.

This framework supports data processing that consists of millions of rows. Also, it is capable of working with clustered virtual machines on Scala. It has been on the list of top AutoML frameworks since its release.


Sequential model-based algorithm configuration or SMAC is a versatile tool for the optimization of algorithm parameters. This framework is very efficient in optimizing hyperparameters. This optimization also helps with machine learning algorithms.

Also, it allows better scaling to high dimensions and discrete input from other algorithms. And most importantly, it can capture and exploit important information about the model domain. This includes details about the input variable. So, it helps the algorithm designers focus on tasks rather than tuning.

9. Amazon Lex

Amazon Lex framework provides the advanced deep learning functionalities of ASR. ASR or automatic speech recognition is necessary for the conversion of speech to text and natural language understanding.

It also allows users to build applications that have high user engagement. Also, it enables Amazon Alexa for all users. It helps them to build bots quickly and easily.


There is no doubt regarding the importance of automatic ML in business. It is really necessary to boost the performance of a business. Here we have explained Automatic Machine Learning (AutoML) frameworks that you should learn.

I hope this article was helpful to you. If you liked the article, share it with your friends. If you have some suggestions, do not hesitate to leave them in the comments section below.

Posted by
Ajoy Kumar

He is a Computer Science graduate dedicated to empowering individuals to forge successful careers in programming and the dynamic world of technology and industry.

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