AutoML is an artificial intelligence model that automates the machine learning process. It was developed to solve problems without the need for machine learning experts. It operates with a high degree of automation, aiming to make tasks simpler by automatically learning and performing repetitive tasks.

What is AutoML?

AutoML stands for "automated machine learning." It plays a role in making traditional, non-automated machine learning algorithms suitable for automation. It classifies and combines algorithms, simplifying them. This eliminates the need for experts in the machine learning process and increases the accessibility of the technology.

In standard machine learning applications, a dataset is used for training, and this dataset may need to be continuously updated. Converting data into a format understandable by the machine, organizing, and updating it typically requires expertise in data science. AutoML simplifies these steps and automates the training process without the need for an expert.

How Does AutoML Work?

AutoML performs the same tasks a data analyst would in the machine learning process. In the automated machine learning process, the following steps occur automatically:

1. Defines the dataset.
2. Selects the most suitable machine learning algorithm.
3. Creates an analytical model suitable for the algorithm.
4. Trains the model using the dataset and makes necessary revisions.
5. Becomes ready to provide outputs once the training process is complete.

AutoML uses two different methods to select the most suitable machine learning algorithm:

1. Researches neural networks.
2. Examines algorithms used in pre-trained models.
    

With AutoML, users working in data science can produce artificial intelligence solutions using simple programming languages like Python without needing expertise in machine learning and deep learning. Although still under development, it has shown excellent results in applications in the literature. Explaining how the system works in these studies is crucial because the accuracy of the outputs obtained with automated machine learning models is verified, allowing for model improvement.

Some functions that AutoML currently performs are listed below:

• Raw data analysis
• Feature selection in engineering models
• Hyperparameter optimization
• Monitoring and issue checking
• Result analysis

Why is AutoML Important?

AutoML is important because it reduces errors and risks associated with the human factor in the machine learning process. Traditional machine learning models can develop through different methods applied by data analysts and trial-and-error. AutoML aims to minimize human intervention by automating the entire process. In other words, it completes the learning process more quickly and accurately with optimal performance from a dataset.

AutoML Tools

Several platforms are offered for AutoML. Some of them are:

• Google AutoML
• Microsoft Azure AutoML
• AutoKeras
• Auto-sklearn

According to experiences, Microsoft Azure AutoML and Auto-sklearn are stronger in self-improvement from existing models. They are also cost-effective due to lower resource consumption. Google AutoML and AutoKeras are more successful in creating new models and better utilizing neural networks.

AutoML is a machine learning model that continues to develop. It aims to automate the learning processes that occupy the time of data analysts. The increasing demand for machine learning and efforts to make the technology accessible to end users highlight the importance of AutoML.