A Brief Introduction to AutoML!

Auto ML- Featured Image

Machine Learning and AI have made their waves in the industry by easing the various market-centric processes and industry-led applications. Sectors like finance, transport, education, retail, and healthcare have been promoting and using machine learning on a large scale and the output has been assuring. 

Even though machine learning has helped many enterprises, there are times when a company or startup is not able to adopt these techniques, citing budget issues.

This is where automated machine learning comes into the picture. Machine learning may involve hiring different experienced people and considering their suggestions on which model to use where. AutoML eliminates this aspect and directly helps the business by deploying a pre-built model in the system and using it for further implementations. This can even be used by people who lack the technical expertise in this field but still want to deploy it successfully. 

Process of AutoML

process- flow

Automated machine learning is the solution to automating the entire process of applying machine learning algorithms to real-world problems. An AutoML model consists of the following steps.

Reading the input data

The input data is retrieved from the source and checked for any redundant or null values.


Preprocessing the data includes feature extraction, feature selection, feature engineering, and feature optimization.


Optimization involves selecting the correct model for our dataset in order to produce the required output and as good as possible. This involves algorithm selection and hyperparameter optimization.


Deploying our created model into the system and checking its efficiency as well. It can reduce the overhead for any company if not the cost at least. This also requires data prediction and generating new techniques in order to run the business successfully. 

Read More: Machine Learning vs Deep Learning – What Makes Them Different


This sounds like a full-proof plan for some startups and potential investors for their businesses to prosper and generate revenue. It is quite incremental for anyone who is already into computer vision and wants to automate the entire process.

A company that is currently dealing with brand logos took the help of TensorFlow to create and train their own model. It garnered an accuracy of 78% which seemed quite good in the beginning. Later, they resorted to AutoML which trained their model and reached an accuracy of 90% which is quite admirable and commendable.

This resulted in a total shift to AutoML and better model implementations. Hence, AutoML has changed the face of machine learning for those who want to apply it in their businesses but are less technically sound. 

AutoML also reduces the overhead by increasing productivity. It is achieved by automating the normal and repetitive tasks. It also avoids the manual errors that may occur with human intervention. It also encourages everyone to easily access all the components of machine learning, thereby causing widespread usage and application of machine learning in various systems.

FrameWorks of AutoML



Auto-sklearn is the automated machine learning package built on top of Scikit-learn. It helps in hyperparameter tuning and algorithm selection for scikit-learn. It also has some feature engineering techniques like PCA and one-hot encoding. The scikit estimators are utilized for taking care of the regression and classification problems. At the moment, it has its application only in Linux systems.

The main components for the hyperparameter optimization include meta-learning for operating the Bayesian optimizer along with automated ensemble construction for optimization. This enables it to perform decently on small and medium-sized data. The only concern arises when it is applied on a big dataset which yields deep learning on a modern scale for a big system.


H2O is an open-source machine learning platform designed and maintained by the startup H2O.ai. It is widely used commercially for deploying both statistical and machine learning algorithms in both R and Python. Popular models like gradient descent, linear ML models and some deep learning models are deployed using this package. 

This package has its own machine learning module with the algorithms in-built to construct and deploy a pipeline. It has the capability of automating a few of the machine learning tasks such as optimizing the model, feature engineering, model validation, selection, fine-tuning and its final deployment. On top of this, it also visualizes the results produced by the model in the pipeline.

Cloud AutoML

Cloud AutoML is offered by Google for machine learning purposes. This is a machine learning engine based on cloud which will help developers who don’t have enough expertise on this topic to train their models with top-of-the-line algorithms and codes to suit their business requirements. Google uses its premium transfer learning and Neural Architecture Search technology to perform the automation in machine learning through the cloud. 

It comes with an intuitive graphical user interface that enables users from all backgrounds to train, evaluate, tweak and deploy the machine learning model based on the requirement of the dataset. There are many different options provided by Google to leverage your business needs. These are:

  • AutoML Vision
  • AutoML Natural Language
  • AutoML Translation

All these products come with a price tag. It all depends on how much time is required for the model to be trained as well as the number of images that are being used to train these models. 


MLBox is an automated powerful Python library that provides an array of vast features. 

It delivers a fast reading of data along with data preprocessing, cleaning and formatting. It has an in-built highly vigorous feature selection feature and on-par hyperparameter optimization. The website Kaggle is the prime example that really performed well under this library. It contains three sub-packages which help in automating the machine learning tasks easily.

Pre-processing includes reading, understanding and pre-processing the given data.

Optimization is testing and validating the model in the system.

Prediction is quite important as it is conducive for predicting the results from the input.


TransmogrifAI is another open-source automated ML library from the company SalesForce. It is their premium platform to test and perform tons of machine learning algorithms which is named “Einstein”. It an end-to-end solution especially for structured data written in Scala on top of Apache Spark. It helps in building modular and reusable machine learning workflows for smooth execution. It can easily train the good machine learning models with minimum tweaking or tuning required. 


Machine Learning has slowly captured the market and made its presence felt by delivering the necessary disruption which the market was asking for a long time. Automating the machine learning process means that we have reached a stage where our hold on machine learning is strong enough and can be deployed in different sectors with minimal risk.

AutoML was basically designed to automate the iterative tasks like the hyperparameter tuning and pipeline creation carried in machine learning. More and more businesses adopting it will only lead to the rise of this technology which will evolve the roots of machine learning as well as Data Science!


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