Automated machine learning (AutoML) is the process ofautomating the tasks of applyingmachine learning to real-world problems. It is the combination of automation and ML.[1]
AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as anartificial intelligence-based solution to the growing challenge of applying machine learning.[2][3] The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.[4]
In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriatedata pre-processing,feature engineering,feature extraction, andfeature selection methods. After these steps, practitioners must then performalgorithm selection andhyperparameter optimization to maximize the predictive performance of their model. If deep learning is used, the architecture of the neural network must also be chosen manually by the machine learning expert.
Each of these steps may be challenging, resulting in significant hurdles to using machine learning. AutoML aims to simplify these steps for non-experts, and to make it easier for them to use machine learning techniques correctly and effectively.
AutoML plays an important role within the broader approach of automatingdata science, which also includes challenging tasks such as data engineering, data exploration and model interpretation and prediction.[5]
There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry".[7] This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms andsystem design.[8]
Additionally, some other challenges include meta-learning challenges[9] and computational resource allocation.
