If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Automated Machine learning is considered as a suitable and comprehensive approach to address and eradicate the challenges associated with machine learning algorithms and models. Automated machine learning ensures end-to-end automation of the ML algorithm and model. It is designed to conduct automated data analysis, so that accurate and précise results can be achieved. Automated Machine learning algorithm unburdens the data scientists, as it not only cleans and collects the data but also automatically trains the models as well. Through its automated feature engineering attribute, AutoML automatically collects the data, extracts meaningful information, and detects any distorted data in the entire process.
With the advent of so many computing and serving frameworks, it is getting stressful day by day for the developers to put a model into production. If the question of what model performs best on my data was not enough, now the question is what framework to choose for serving a model trained with Sklearn or LightGBM or PyTorch. And new frameworks are being added as each day passes. So is it imperative for a Data Scientist to learn a different framework because a Data Engineer is comfortable with that, or conversely, does a Data Engineer need to learn a new platform that the Data Scientist favors? Add to that the factor of speed and performance that these various frameworks offer, and the question suddenly becomes even more complicated.