Ten Mistakes to Avoid in the Machine Learning Life Cycle

Although many companies are excited about machine learning, they often overlook some key success factors. To succeed in machine learning, enterprises must embrace the full machine learning life cycle in a unified way, from data management and governance to data engineering to building the model and putting it into production while ensuring that the organizational culture embraces predictive applications. Read this TDWI report to learn the common mistakes to avoid when building and deploying machine learning programs.