What is Automated Machine Learning (AutoML): A Comprehensive Guide

AutoML automates the ML process, including model selection and tuning, and data preprocessing. It helps speed up ML, boosts model accuracy, and makes ML easier for non-experts.

Published on:

April 29, 2024

What is AutoML?

Automated Machine Learning (AutoML) refers to using various algorithms to select and optimize ML models automatically. AutoML aims to make machine learning more accessible to non-experts by reducing the knowledge and expertise needed to apply ML to a given problem. It involves automating the end-to-end process of applying machine learning to a specific problem, including the automatic selection and tuning of models and data preprocessing.

AutoML is used to speed up the ML process by reducing manual tasks. It is used to improve the accuracy of models by selecting the best algorithms and hyperparameters on their own. 

Machine learning libraries and platforms often make the entire process of ML relatively easier, right from handling raw datasets to developing a practical ML model. And the traditional ML process involves developing these models by hand and managing each step separately. 

However, solving real-world business problems using traditional methods takes time and resources. On the other hand, automated machine learning makes it easier for users to develop and deploy models by systematically processing raw data and choosing the models that get the most relevant information. 

What are the benefits of AutoML?

AutoML focuses on creating algorithms that can automatically build and optimize ML models. It can optimize various machine learning models like regression, classification, and clustering. These algorithms can automatically select the best machine learning algorithm for a given dataset and task and optimize the chosen algorithm's hyperparameters.

AutoML can offer several benefits, including: 

  1. Increased efficiency: Automated Machine Learning can enhance the end-to-end ML process by reducing the need for manual intervention.  
  2. Improved accuracy: AutoML systems can select and tune models automatically to achieve better performance.
  3. Reduced human inputs: It reduces the need for human inputs by automating the entire ML process to real-world scenarios. 
  4.  Reproducibility: Automated machine learning process can make it easier to reproduce the results and allows for better traceability of decisions. 
  5. Scalability: AutoML can easily handle big data and large-scale problems and could be used for hyperparameter optimization on distributed systems.
  6. Improved data quality: AutoML can enhance data quality by automatically pre-processing and selecting the best algorithms. 

Different methods of AutoML

There are three main types of AutoML:

  1. Model pre-training
  2. Model tuning
  3. Model generation

Model Pre-Training

This is a process of automatically selecting and training an ML model using data that has already been pre-processed. Model pre-training is used when you want to automatically determine the best machine-learning algorithm for a given problem, as it can automatically optimize the hyperparameters of the chosen algorithm.

Model Tuning

This process automatically adjusts a machine learning model’s parameters to improve performance. Model tuning is specifically used to select the best ML algorithm for a given problem and can also automatically improve the chosen algorithm's hyperparameters.

Model Generation

This is the process where you can build a machine learning model from scratch. 

How to use AutoML

There are several ways to use AutoML depending on the specific problem and the available tools, but generally, the process can be broken down into the following steps:

  1.  Define the problem: Clearly define the problem you want to solve, including the inputs, outputs, and any specific constraints or requirements. 
  2. Prepare the data: Gather and prepare the required data to train and test your models. This may include cleaning, preprocessing, data splitting, validation, and testing. 
  3. Choose an AutoML platform or tool: Many AutoML platforms and tools are available, like Attri
  4. Configure the AutoML system: Depending on the platform or tool you have chosen, the next step would be to configure various settings, like different types of models to search over, the range of hyperparameters to explore, and any other settings that are relevant to your problem. 
  5. Train and evaluate models: Run the AutoML system and let it train and assess various models. The system will automatically select and tune the best models based on the performance of the validation set. 
  6. Select the best model and make predictions: Once the training and evaluation process is complete, select the best model and use it to make predictions on new data. You may also be required to evaluate the performance of the best model on a test set. 
  7. Monitor and retrain: Once you have deployed your model, monitor its performance over time and retrain it as necessary if the performance drops or new data is available.

What are the Pros and Cons of using AutoML?

AutoML is a rapidly growing segment of artificial intelligence (AI) that automates creating, tuning, and deploying of ML models. It can democratize machine learning, making it accessible to non-experts who don’t have sound knowledge about it. However, there are also some potential drawbacks to consider as well.


  1. It is more efficient and saves time as it can automatically select the ML models and optimize their performance.
  2. It can be used for various tasks, including but not limited to classification, regression, prediction, and more. 
  3. AutoML enables non-experts to apply machine learning to a given problem without requiring a deep understanding of the underlying algorithms and techniques. 
  4. It makes it easier to reproduce the results and allows better traceability of decisions. 


  1. Initially, it can be time-consuming and labor-intensive as it involves using algorithms to select and optimize the ml models automatically.
  2. AutoML systems can be complex and may need to provide the complete control and flexibility that an expert generally has while manually selecting and tuning the models. 
  3. AutoML tools can be costly, especially for complex and significant scale problems. It may also require some computational resources. 
  4. AutoML is as good as the quality of data. If the data is clean, it may produce accurate results. 

Where Can AutoML Tech Be Used?

AutoML can be used in a variety of areas; some examples include the following: 

  1. Computer vision: AutoML can automate tasks like object detection, image classification, image segmentation, and more. 
  2. Natural Language Processing (NLP): It automates text classification, language translation, and generation. 
  3. Time-series analysis: AutoML can be used for forecasting, anomaly detection, and other time-series analysis tasks. 
  4. Recommender systems: AutoML can build personalized recommendations for users by automating selecting and tuning models. 
  5. Healthcare: AutoML can analyze patient data for diagnosis, treatment planning, and drug discovery. 
  6. Finance: Used for fraud detection, portfolio optimization, and other financial analysis tasks. 
  7. Customer Relationship Management (CRM): AutoML can predict customer behavior, optimize marketing campaigns, and improve customer retention. 
  8. Supply Chain: AutoML can be used to improve the forecasting of demand forecasting, inventory, and logistics optimization. 
  9. Internet of Things (IoT): AutoML can process sensor data and make predictions based on that data. 
  10. Self-driving cars: AutoML can be used to improve the performance of perception and decision-making systems in autonomous vehicles. 

This list is incomplete, AutoML can be used in many other areas where machine learning is applied, and the technology is rapidly evolving. Its ability to automate the process of applying machine learning to a problem can make it a powerful tool for organizations looking to leverage machine learning to improve their operations and gain a competitive advantage.

About Attri

Attri is an open-source MLOPS framework that allows you to build, deploy, monitor, and optimize ML models into production quickly and cost-effectively. You can easily scale your machine learning development from research to production with an E2E solution giving you all the tools in one place. Attri’s pre-built industry-specific AI-powered blueprints enable you to integrate your ML pipelines with any application. 

Attri has a lot to offer more. Learn more about it by getting in touch with us