Nearly everyone has heard of or used artificial intelligence (AI) and machine learning (ML) in some form or fashion. Some students are already publishing papers in the field, while others apply various AI techniques in their research, internships, or just for fun. Professionals in the industry have either incorporated some form of AI/ML into their products or services or are considering it. Either way, AI and ML have much to offer without a good amount of data, significant processing power, the right skill sets, and patience with the design and execution of such projects. For that problem, AutoML is a promising new technique that allows researchers and professionals to use pre-trained models and cloud-based services to roll out AI solutions much more rapidly than building machine learning models from scratch. AutoML provides the methods and processes to apply, integrate, deploy, and scale machine learning intelligence without requiring expert knowledge. Major AI platforms, starting with Google and followed by Microsoft, H20.ai, and others, are priming AutoML as the next evolutionary frontier in artificial intelligence. Machine-learning practitioners can then spend zero time recreating machine-learning models from scratch and, instead, focus on applying the models while letting machines take care of building them.
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