Although the concepts of machine and deep learning have been around since the 1950s, increases in both the amount of raw data that organizations store and processing power available in the last decade has led to increased interest and advances. Automation has already been proven to improve productivity – both in businesses and across entire sectors. Software has driven much of this automation process, but many workflows still require decisions to be performed by humans. Machine and deep learning aim to automate the decision-making process by training algorithms to take over, using empirical evidence from stored data. How can today’s engineers take advantage of modern machine learning methods? What are the pitfalls that can occur when trying to automate decision-making? How can businesses take advantage of the hoards of data that they have accumulated?