What is Auto ML?

Automated Machine Learning (AutoML) is a process that uses machine learning models to solve real-world problems automatically. In AutoML systems choosing the right model to train, deciding on training parameters, etc are all automatically taken care of by the system. In conventional machine learning, complex tasks such as data preprocessing, model selection, and optimization must be implemented manually. The advantage of AutoML is that most of these steps can be performed without the need for an expert. As a result, machine learning engineers can focus on other sectors based on their domain knowledge. In addition, machine learning models can be used by people without much experience in the field. Because of this, business owners are able to utilize artificial intelligence in a variety of ways to achieve their goals with automated machine learning processes.

Differences between Conventional ML and AutoML

In technical terms, the only difference is that some parameters that had previously been fixed and unchanged have now been learned. In the simplest case, AutoML can search for all possible hyperparameters and models. Machine learning models are based on trial and error. There is no way to be sure which set of parameters will work well. AutoML’s process involves a lot of trial and error and the results should be constantly analyzed. Considering the fact that implementing all these tests will be costly, you might ask how much improvement is AutoML offering compared to random search and grid search. The AutoML implementation method answers this question.

Evolutionary algorithms, gradient-based optimization, and Bayesian optimization are some of the implementation methods that seek to provide an optimal and effective AutoML experience.

Levels of AutoML

AutoML is now divided into different levels by some specialists [1]. The methods of AutoML could be adjusted according to the type of machine learning project in order to maximize its benefits and minimize its limitations.

  • Level 0: No automation. You code your own ML algorithms. From scratch. In C++.
  • Level 1: Use of high-level algorithm APIs. Sklearn, Keras, Pandas, H2O, XGBoost, etc.
  • Level 2: Automatic hyperparameter tuning and ensembling. Basic model selection.
  • Level 3: Automatic (technical) feature engineering and feature selection, technical data augmentation, GUI.
  • Level 4: Automatic domain and problem-specific feature engineering, data augmentation, and data integration.
  • Level 5: Full ML Automation. Ability to come up with super-human strategies for solving hard ML problems without any input or guidance. Fully conversational interaction with the human user.

Advantages of autoML

  • Making complex technologies accessible (simplicity)
  • Less need for expert staff (simplicity)
  • Cost-savings
  • Developing better models
  • A faster way to find the best model (speed)

AutoML challenges

The purpose of AutoML is to simplify and accelerate machine learning modeling. Nevertheless, AutoML comes with a number of challenges. First, “How can we ensure that the resulting AutoML model is accurate?” For many AutoML users, the question is: “Is this model the best possible?”. Therefore, many AutoML technologies and solutions support the fine-tuning of Hyperparameters. As a result, users can create several different models and compare them. Having the ability to manipulate Hyperparameters is nice, but some argue that it eliminates AutoML’s most important advantages: simplicity and speed.

Moreover, the usual AutoML technology will remain dependent on technical knowledge. When implementing AutoML, a machine learning engineer often has to check and find the best model or models to implement. The machine learning engineer may also use other technologies to build the final model if several models perform well during the evaluation phase. This approach would also undermine AutoML’s goal of simplifying and accelerating machine learning.

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