5 ways to improve AI/ML deployments

In January 2019, Gartner released a survey where 37% of respondents said they were already using artificial intelligence (AI) in some capacity, but 54% of respondents reported skills shortages in their organisations that prevented them from moving forward with AI more aggressively. This is not referring to data scientists, who continue to be in short demand and are aggressively being hired, rather to the fact that many organisations do not operational their AI efforts with IT project methodologies to ensure that projects meet their business goals. "What we are seeing is a lot of data science teams that are working on many concurrent ML and AI initiatives, but fewer that have deployed the models into actual production applications," said Nathaniel Gates, CEO of Alegion, which specializes in training machine learning (ML) data. Gates added that highly skilled data scientists may lack practical business experience in data preparation and project management. "These people are skilled at conceptualizing, building out, and testing AI and ML algorithms," he continued.