What is AutoML in Machine Learning?

soham Medewar
Last Updated: May 13, 2022


We know that machine learning, deep learning are very vast topics. Learning everything is quite complicated, so scientists came up with a terminology called autoML. Automated Machine Learning (autoML) is a technology where users can create machine learning models of their own without prior knowledge of machine learning algorithms.

Why need autoML?

As we know that skills and computing resources are two main barriers to use machine learning. The first barrier requires data scientists, which are very difficult to hire and demand hefty salaries, and the second one requires accelerated hardware(such as computers with high GPU’s). Crossing these two barriers takes lots of time as well as money. 

AutoML saves us from both obstacles. AutoML makes it easier to train the model without heavy system requirements or professional skills. The user needs to provide labeled training data as input and receive an optimized model as output.

How does autoML work?

AutoML aims to automate the entire process starting from data filtering to parameter optimization. AutoML follows the below four steps. 

  • Data Filtering
  • Data pre-processing and feature selection
  • Model selection
  • Hyperparameter tuning (parameter optimization). 

Till now, autoML has mainly focused on model selection and hyperparameter tuning. The brute force approach in the model selection process is to select every possible algorithm and train it using the prepared training dataset. Calculate the accuracy of every model and consider the best one. In the hyperparameter tuning process, the selected model undergoes parameter optimization where the best parameters are decided to get the best accuracy. 

Advantages of autoML

1. Saves time

Choosing an appropriate model and hyperparameters for solving the problem takes a lot of time for an average person. On the first try, no one can predict the best fit model for a problem; they need to analyze data and apply different algorithms. After choosing the correct algorithm, they need to optimize the hyperparameters; finally, they will come up with the best model. AutoML does all this work on its own. The user only needs to give training data as input, and the user will get the best model. So hours of work will be done in just a couple of minutes.

2. Bridges skill gap

As we know, learning everything in Machine Learning is entirely challenging. If a person doesn't have sufficient knowledge about any algorithm, it will be challenging to implement a model. Lack of skills generates a gap between the people and the technology. AutoML fulfills this gap. It is not compulsory to know a particular algorithm; without knowing, you can implement the model without any difficulties. 

3. Increases Productivity

Thinking over a problem, selecting its main features, deciding a proper algorithm for a model, and changing its hyperparameters takes lots of human effort, leading to slow production. In autoML, providing input data to the machine is enough to do all the work. It gives every information that a user wants within a couple of minutes; this leads to faster production.

4. Error Reduction

Manual implementation of the Machine Learning model can not guarantee the maximum correctness of the model so, a minimal error cannot be achieved. In the case of auto ml, all the possible causes will be taken in order to minimize the error. The model obtained as output from autoML will be of least error. In this was autoML helps in error reduction. 

Use cases of autoML

AutoML has its application at the following places. 

Time Series Forecasting

Time series forecasting predicts future events by analyzing the events that happened in the past. Time series analysis is very laborious and complicated. Time series problems include weather forecasting, stock market price prediction, etc. These ML models are needed to update over a period of time as the environment changes. AutoML entirely automates the forecasting process, from feature selection to hyperparameter tuning is done by autoML. It automatically detects noise, stationarity, seasonality, and patterns. So it can automatically update the model. In this way, auto ml helps in time series forecasting. 

Classification Problem

Classification problems lie under supervised machine learning. Type of class is predicted in classification problems. Common examples of classification problems are spam email detection, cancer detection, emotion detection, dog vs. cat classification. Google autoML helps to automatically deploy the model. 

Regression Problem

Like classification problem, regression problem lies in supervised machine learning. But regression models predict numeric values. Common examples of classification problems are house price prediction, sales price prediction. AutoML Tables will help you to train machine learning models on the tabular data to make predictions.

Feature Selection

Features are the predictors of the machine learning model. Appropriate features must be selected in order to get the best model. Suppose we have more features in the training dataset. Selecting the most appropriate features is one of the difficult work. AutoML handles the work efficiently; it tells which feature should be included to get the best model.

Algorithm Selection

The main part of the machine learning model is to choose a suitable algorithm. A perfect algorithm must be used for model preparation to get the best model. For example, classification problems can use the following algorithms: support vector machine, logistic regression, decision trees, random forest, etc., so we must decide the suitable algorithm according to the dataset. AutoML-Zero helps the user to select the best model for the particular dataset.

Model Tuning

After selecting the best algorithm, optimizing hyperparameters is necessary. Proper hyperparameters increase the accuracy of the algorithms. Each model has many hyperparameters; we have to try for a large number of combinations in order to get optimal accuracy. AutoML finds the best hyperparameters for the model automatically.

Top organizations providing autoML facility

There are several organizations, have contributed to cloud-based autoML platforms.

Top three autoML platform

Amazon sagemaker auto-pilot

Sagemaker auto-pilot is part of the AWS framework. It provides tools and a platform for the entire Machine Learning cycle.

Google cloud autoML

Google provides Google cloud autoML; it provides solutions for natural language processing, computer vision, and tables. 

Microsoft azure autoML

Microsoft azure autoML recommends the user to work mainly on classification, regression, and time-series prediction models.

Other companies like IBM, H2O.ai, Salesforce, SAP, Tencent, Aible, Prevision.io provide autoML features.


  1. What can autoML be used for?
    AutoML is designed to perform tasks efficiently with accuracy and precision. AutoML automates, like monitoring, analysis, and problem detection, are rote tasks faster if automated.
  2. Are AutoML tables free?
    You can try AutoML Tables for free by using six free node hours for training and batch prediction per billing account.
  3. Where is autoML used?
    Many companies now offer AutoML as a service, where a dataset is uploaded, and a model pipeline can be downloaded or hosted and used via web service (i.e., MLaaS). Famous examples include service offerings from Google, Microsoft, and Amazon.

Key Takeaways

  • In the above article, we have learned about the functioning of autoML.
  • How does autoML save time and money?
  • Advantages of autoML.
  • Classification, Regression, Hyperparameter tuning, Algorithm selection, etc. using autoML.

Want to learn more about Machine Learning? Here is an excellent course that can guide you in learning. 

Happy Coding!

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