Streamlining the Production of Artificial Intelligence - DATAVERSITY

People often think the algorithms used for Machine Learning (ML) are the most important factors for developing a successful ML system. However, shrewd Artificial Intelligence (AI) and Machine Learning systems in production (managing the data at all stages, with multiple models) have much more impact on the success of the model than the specific learning algorithm. In their book AI and Analytics in Production, Ted Dunning and Ellen Friedman describe how organizations can get their AI systems into production and delivering value. "In addition to the platform and application orchestration technologies, you will need an architectural design that simplifies logistics, supports multiple models and multiple teams easily, and gives you agility to respond quickly as the world (and data) changes, as indeed it will." Database storage has been determined by specific processes that assured accessibility, security, and accuracy, however, increasing amounts of unstructured data and the increased use of data lakes has caused significant problems in Data Management.