Make Machine Learning Work for Your Company: A Primer

Over the last 50 years, machine learning (ML) has evolved through a series of hype cycles -- periods of public fervor as well as funding droughts known as "AI winters" -- to reach mainstream applicability and acceptance. With recent computing advances, we now see machine learning being widely used for things like search and feed ranking, spam filtering, and warnings about suspicious credit card activity. A specific form of ML called Deep Learning has fueled the recent growth in Natural Language Processing (NLP), autonomous driving, image and object recognition, and virtual personal assistants. Now, machine learning has evolved to the point where it won't just be integrated into new products but will also transform how products are built. Already today, ML offers enough benefits for product development that most companies should consider incorporating it into their processes. But when does it make sense to invest in machine learning capabilities and how do you actually build a machine learning team?