Automated Machine Learning

Automated machine learning, or AutoML, is a way of simplifying the tasks involved in applying machine learning to real-world problems. It can automate the steps such as data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation.

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Automated Machine Learning (AutoML) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. It involves automating the selection and tuning of machine learning models to create a streamlined and efficient approach for individuals with varying levels of expertise to implement machine learning solutions.

It allows data scientists, analysts, and developers to build machine learning models with high scale, efficiency, and productivity, all while sustaining model quality.

Automated Machine Learning has gained traction, with statistics showcasing its impact: According to Gartner, by 2024, 75% of enterprises are expected to shift from manual to automated machine learning operations.

Why is AutoML used?

AutoML is used for several reasons, including:

  1. Simplifying Complexity: Automating complex machine learning tasks to make them accessible to individuals with limited expertise.
  1. Efficiency: Streamlining the machine learning model development process, saving time and resources.
  1. Wider Adoption: Allowing non-experts to leverage the power of machine learning for various applications.

What is an AutoML example?

An example of AutoML is the use of platforms like Google AutoML, where users input their dataset, and the system automatically selects and tunes the most appropriate machine learning model for the given task, whether it's classification, regression, or other applications.

What is the process of AutoML?

During training, AutoML creates a number of pipelines in parallel that try different algorithms and parameters. The service iterates through machine learning algorithms paired with feature selections, where each iteration produces a model with a training score. 

The better the score for the metric you want to optimize for, the better the model is considered to “fit” your data

The AutoML process typically involves the following steps:

  1. Data Preprocessing: Handling missing data, encoding categorical variables, and scaling features.
  1. Feature Engineering: Creating new features or transforming existing ones to improve model performance.
  1. Model Selection: Automatically selecting the most appropriate machine learning model for the given task.
  1. Hyperparameter Tuning: Adjusting the model's hyperparameters to optimize performance.
  1. Evaluation: Assessing the model's performance on a validation dataset.

Who created AutoML?

The concept of AutoML has been developed and implemented by researchers and organizations in the machine learning community. Various platforms and frameworks, including Google AutoML, provide tools to implement AutoML processes.

The first group was founded in 2013 by Prof. Hutter as an Emmy-Noether research group, where Prof. Lindauer joined in 2014 as a postdoc, before founding his own group in 2019 in Hannover

Who should use AutoML?

AutoML can be used by software engineers to develop applications without the need to know the details of working machine learning algorithms, data scientists to build machine learning pipelines in a low-code environment, machine learning engineers to speed up their work, and AI enthusiasts to explore the capabilities of AutoML. AutoML is designed for a broad audience, including:

  1. Data Scientists: To streamline and automate parts of the machine learning workflow.
  1. Business Analysts: To leverage machine learning without extensive technical expertise.
  1. Researchers: To quickly experiment with different machine learning models.

Is AutoML the future?

AutoML is considered a significant development in the field of machine learning and is likely to play a crucial role in the future. As technology advances, AutoML is expected to become more sophisticated, making machine learning more accessible to a broader audience.

AutoML is used to make machine learning more accessible, improve the efficiency of machine learning systems, and accelerate research and AI application development

What algorithm does AutoML use?

AutoML can use a variety of algorithms, depending on the specific task and the platform used. It can encompass a range of algorithms, from traditional machine learning models to deep learning approaches.

What is the limit of AutoML?

The limit of AutoML can vary depending on the platform being used.

For instance, Azure Machine Learning’s AutoML has certain limits on the number of iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model

Is AutoML free?

The availability of free AutoML services depends on the platform. Some AutoML tools offer free versions with limited capabilities, while others may require a subscription or payment for more advanced features.

While some platforms like Google Cloud offer free credits to get started with AutoML, others like Azure Machine Learning offer a free trial period. However, most platforms operate on a pay-as-you-go pricing model.

What is Google AutoML?

Google AutoML is a suite of machine learning tools offered by Google Cloud Platform. It allows users to build and deploy machine learning models without the need for extensive expertise in machine learning or programming.

Is AutoML supervised or unsupervised?

AutoML can be applied to both supervised and unsupervised learning tasks. In supervised learning, it automates the process of selecting and tuning models for tasks such as classification or regression. In unsupervised learning, it can be used for clustering or dimensionality reduction.

Examples of automated machine learning

  1. Google AutoML Vision: Automatically classifies images and detects objects in photos with minimal manual intervention.
  1. Driverless AI: Provides an automated platform for data science and machine learning, automating feature engineering, model selection, and hyperparameter tuning.
  1. DataRobot: Offers an end-to-end automated machine learning platform that enables users to build and deploy machine learning models without extensive coding.

Related terms

  1. Machine Learning: A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
  1. Artificial Intelligence: The simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
  1. Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
  1. Deep Learning: A subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.


In conclusion, Automated Machine Learning represents a significant advancement in the democratization of machine learning, making it accessible to a broader audience. The ability to automate complex tasks in the machine learning workflow, from data preprocessing to model selection and tuning, has the potential to revolutionize how organizations leverage machine learning for various applications. 

Platforms like Google AutoML exemplify the power of automation in simplifying the implementation of machine learning models. As technology continues to evolve, the role of Automated Machine Learning is likely to expand, enabling more individuals, including those without extensive technical backgrounds, to harness the benefits of machine learning in their respective domains. 

Adopting Automated Machine Learning can lead to increased efficiency, faster model deployment, and broader innovation in the rapidly evolving landscape of artificial intelligence and data science.



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