Author Chris Cozzi

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap, which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production. Deployment and Monitoring Challenges Our DataRobot…

Although the fields of machine learning and AI have begun to mature, there is often a lack of success in applying AI in organizations, an issue DataRobot calls the "AI production gap." One of the leading common issues we see in the machine learning life cycle (feature engineering, training, scoring, etc.) is the involvement of a variety of separate and…

Most organizations invested in machine learning are experiencing what we at DataRobot call the AI production gap, which refers to the transition of models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production. Production AI Challenges Enterprises increasingly rely on AI and machine learning to…