Automated Machine Learning with Python: A Comparison of Different Approaches

These four automated machine learning tools will help you build ML models quickly for your Data Science projects.

Automated Machine Learning with Python: A Comparison of Different Approaches
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With the increase in the data of the more prominent organization, people showed to understand the pattern of sales, marketing, etc., which was being formulated by the vast data being available in the particular organization, people were more and more inclined to learn Machine learning and Data analysis than ever before and this urge is going to retain even post-pandemic.

While going through Data handling and hyperparameter tuning for Machine Learning projects, you would have felt the thirst for an automated method that would have saved your time from the conceivable exhaustive process of tuning the billion of parameters followed by trying and testing the different models, which would adequately fit your training dataset.

The answer to this need is YES; in today's world, many such tools would not only automate the data handling stage but helps in choosing the relevant model for predictive analysis on the testing dataset.


Automated Machine Learning with Python: a Comparison of different Approaches
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Therefore, the is a need for Automated Machine learning (AutoML).

So, in this article, I will give you a brief idea about AutoML in the present times.


What is AutoML?


In simple terms, you can think of Automated machine learning as applying Machine Learning (ML) models to real-world problems by only initiating the process by running specific commands. Eventually, the rest of the work, pipelines, will be taken care of. Specifically, this process automates several steps in the general machine-learning pipeline, such as choosing the best model for our dataset, hyperparameter tuning using cross-validation, etc. Suppose we are curious about thinking of internal working. In that case, the tool will create different pipelines by choosing the different values of hyperparameters and then selecting the pipeline that provides better evaluation metrics on the test dataset.


Comparison of various AutoML Platforms


Open-source and enterprise AutoML solutions differ significantly: open-source solutions can only automate algorithm selection and hyperparameter tuning, whereas enterprise solutions can do much more (see section "What can we expect from an AutoML tool"). Furthermore, the results obtained with open-source solutions are far inferior to those obtained with enterprise solutions.

Google Cloud AutoML, Microsoft Azure AutoML,, and TPOT are popular automated machine learning (AutoML) tools that provide an easier way to build and deploy machine learning models without requiring coding and data science expertise. However, each tool has its strengths and limitations.


Google Cloud AutoML


  1. Because of its user-friendly interface and high performance, Google Cloud AutoML has      grown in popularity.
  2. In minutes, you can create your custom machine-learning model.
  3. This platform integrates well with various Google Cloud services, which provides scalability and is easy to use from the user's point of view.
  4. To find the example code, follow this Link


Microsoft Azure AutoML


  1. Azure AutoML provides a transparent model selection process for users unfamiliar with coding.
  2. It is a cloud-based service that allows you to create and manage machine learning solutions. Azure as a platform can be learned with prior programming experience.
  3. This platform has good integration with various Azure services, which eventually can run on GPU instances, and as a result, we can quickly deploy.
  4. To find the example code, follow this Link


  1. This company provides an open-source package and a commercial AutoML service called Driverless AI.
  2. This platform has been widely adopted in financial services and retail industries since its inception.
  3. It enables businesses to develop world-class AI models and applications rapidly.
  4. This platform is entirely Open source, provides many algorithms to work with, and is suitable for handling big data regarding velocity and volume, etc.
  5. To find the example code, follow this Link 




  1. TPOT (Tree-based Pipeline Optimization Tool) is a free Python package.
  2. Despite being free, the package has achieved outstanding results in various datasets, including around 97% accuracy for the Iris dataset, 98% for MNIST digit recognition, and around 10 Mean squared error (MSE) for Boston Housing Prices prediction.
  3. This platform is entirely Open source gives very high results in terms of accuracy, and it is swift to work with a high volume of data.
  4. To find the example code, follow this Link


My Verdict on choosing the AutoML Platform


H20, in my opinion, is the best open-source platform for democratizing machine learning. Its comprehensive scope and the H2O Flow web-based interface place it first among open-source solutions. I created a machine learning project for customer churn from the ground up without writing a single line of code.

H20 Driverless AI is the most comprehensive, customizable, and agnostic enterprise solution. While maintaining high control and understanding of the modeling, I quickly generated a model for customer churn that was better than the one from H20-3.

In conclusion, I hope you have enjoyed this article and found it informative. If you have any suggestions or feedback, please contact me via LinkedIn.

Aryan Garg is a B.Tech. Electrical Engineering student, currently in the final year of his undergrad. His interest lies in the field of Web Development and Machine Learning. He have pursued this interest and am eager to work more in these directions.