A Pragmatic Introduction to Machine Learning for DevOps Engineers - OpenCredo

Machine Learning is a hot topic these days, as can be seen from search trends. It was the success of Deepmind and AlphaGo in 2016 that really brought machine learning to the attention of the wider community and the world at large. Yet it's a success that followed a long preamble that includes recent advances in three key areas: hardware, particularly GPUs (ideally suited to the vector and matrix based mathematics usually required in machine learning); data, due to the accessibility of larger and larger datasets; and algorithms and techniques, as deep learning research breakthroughs like those described in Krizhevsky, Sutskever and Hinton's landmark paper began to demonstrate best-of-breed results on benchmark challenges. So it's not just hype, and as IT engineers it's worth our while to gain better understanding of it. But the field can seem rather daunting to a newcomer due to all the math, statistics and algorithms involved.