What Is AutoML (Automated Machine Learning)? Definition and Applications

Augmented reality

Automated machine learning is a new field that is taking off fast. But what is it, and how is it applied?

As with all new tech phenomena, an overview of applications first requires a look at the development itself and where it came from. Let’s take a closer look at this exciting new feature of machine learning, see how it came about, and find out where it’s heading.

What is ML?

Artificial intelligence has a number of different directions and ML is an important one. This is a technology that manipulates algorithms used in data processing to improve performance. 

Machine Learning 

Automated machine learning, then, is a technology that takes simplification of machine learning processes even further by fully automating them. Specifically, AutoML automates the selection, composition, and parameterization of machine learning models. Its goal is to help improve data-driven decision making.

Those in need of assistance can find an AutoML platform that can help make things easier. 

AutoML takes the following steps in its functions:

1. It produces model algorithms.

Every function that is carried out by machine learning (machine learning operations or MLOps) utilizes algorithms to manipulate data by means of coding. The operations produce models that formulate patterns found in the data and are used to make predictions.

2. It optimizes hyperparameters. 

Also known as hyperparameter tuning, hyperparameter optimization is the process of taking a set of hyperparameters (or parameters whose value is used to control the learning process) and tuning them to predict results more accurately. A parameter is a configuration variable of a given model whose value can be estimated from its data.

The way that hyperparameter optimization takes place is by means of cross-validation. 

3. It models iterations. 

As the name suggests, modeling iteration is the process of repeating a set of tasks to achieve a particular result. Modeling iterations involves several different phases:

    1. Fitting parameters. Fitting parameters takes place by means of using “gradient descent” algorithms. Gradient descent works to calculate the “loss” of making incorrect predictions.
    2. Tuning hyperparameters. Hyperparameters are parameters whose values are used as controls for a given process. Modeling iterations tunes these hyperparameters to create accurate standards for larger processes.

Applying models more widely

Models that are used in machine learning are categorized into “families.” Model families are categories of models with customizable structures. It is possible that different families could work in different ways on any given problem. Therefore, AutoML tests families to see what results they bring, depending on data type, amount, and other factors.

1. Ensembling models. 

Once models are created and tested, they are assembled into groups of two or more in order to synthesize the results and thereby produce better predictions. 

2. Tweaking results

Once models are assembled and their results analyzed, it becomes clearer what changes need to be made so that models can be optimized.

What AutoML does in modeling iterations is process all of these functions automatically. In doing so, it helps eliminate human error in the machine learning processes. 

Several benefits can be gained from AutoML:

  1. Model types and their associated algorithms will become clearer. This, in turn, makes their applicability to new problems easier.
  2. Workflows will become more streamlined as cumbersome and laborious manual tasks become eliminated.
  3. Automation will reduce the amount of “black box” criticism that AI has been receiving since its emergence. What this refers to is the fact that algorithms, once set in place, can be difficult to change and track, which can result in skewed outcomes.
  4. Users will be able to handle larger workloads.
  5. Applications to many different spheres will improve quality of life on many levels.

AutoML is used to make improvements in quite a few areas, including:

  • Healthcare. Diagnostic predictions will be refined and made more accurate, and the more wide-scale aspects of healthcare will become clearer with greater insights into disease patterns, patient outcomes, and the need for different kinds of interventions.
  • Consumer behavior. AutoML will assist in tracking and predicting consumer behavior to create more accurate market studies and better predictions for consumerism.
  • Supply chains. AutoML will be utilized to track supply chain patterns and assist in the streamlining of logistics operations. 
  • Language analyses. AutoML will replace limited human coding efforts in producing language analyses that only cover partial language aspects.

The Future is Bright, but Caution Should Still be Exercised 

As can be seen, AutoML stands to make improvements in many different aspects of life. It is not without issues itself, as its relatively untested nature leaves questions open about potential problems that might emerge. One concern is the lack of regulation and oversight. As with many new technologies that have yet to be standardized, AutoML could race out of control faster than humans can keep up with it, and this could result in security problems or other issues.

Another potential issue is accessibility among general users. Even if something is automated, it should be capable of being understood by the people manipulating it, or results or future applications could become incomprehensible.

Nonetheless, the promise that these automated functions possess is great, and in the next several years we should see great advancements in existing and new spheres of its applications.


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