9 pitfalls to avoid in building a successful machine learning program

During my past two decades working in the IT field, I've seen artificial intelligence technologies move from conceptual to practical -- with machine learning techniques at the forefront, becoming more accessible, even for teams without specialized expertise. With increased use of predictive modeling across a wide variety of teams, it's critical for leaders and managers to be aware of common issues that can distort the results of their teams' work. Here are nine common pitfalls to avoid, and best practices to follow, for a reliable machine learning process. The starting point of any machine learning program is to select the training data. Typically, organizations have some data available or can identify relevant external suppliers, such as government entities or industry associations.