Automated machine learning or AutoML explained

The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.