A brief overview of AutoML

The automated machine learning (AutoML) is the process of end-to-end automating the process of applying machine learning. When we apply machine learning algorithms, we usually do data pre-processing, feature engineering, model selection, model training, hyper-parameter tuning and then predictions.

As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the challenge of applying machine learning algorithms. In other words, AutoML is a series of concepts and techniques used to automate these processes.

In the traditional machine learning workflow, there are many teams involved starting from Data Acquisition to predictions. Data engineers work on Data acquisition and data preparation. Data scientists work on experimentation and optimization of models. DevOps team work on development environment and hosting the models in production. AutoML is going to change the above processes. AutoML focuses on two aspects, that is — Data acquisition and prediction. All the steps that take place in between these two are going to be abstracted by AutoML.

Benefits of AutoML:

• For applying machine learning algorithms to the real world problems, we need experts having computer science skills, domain expertise and mathematical expertise. AutoML can be applied by non-experts also.
• AutoML reduces bias and errors that occur while manually designing machine learning models.
• Organization can also reduce the cost of hiring industry experts by applying AutoML.
• AutoML also reduces the time required to develop a machine learning model and to test a model.

Drawbacks of AutoML:

• As AutoML is a new concept in machine learning world, it is therefore important to exercise caution while applying AutoML solutions.
• Another major challenge is the time taken to run the AutoML models. It will depend on the computational power of the machine we are running.