Automation has improved productivity across entire sectors. Software has driven much of this automation, but many workflows still require decisions by humans.
The promise of machine learning is to automate the decision-making process by training algorithms, based on empirical evidence. That promise is becoming very real and tangible for developers who are now able to leverage massive amounts of data with cloud computing power via learning libraries like TensorFlow and frameworks like MXNet.
How can today's engineers take advantage of modern learning methods?
What are the main ideas and pitfalls when trying to automate decisions?
How can organizations harness the power of machine learning to power their business?