ModelOps: Maximizing the Value of Machine Learning and Analytics

Organizations across industries are turning to machine learning (ML) to derive the greatest impact from their data, and maximize new opportunities that can set them apart from their competitors. However, businesses must develop a process that helps push their ML models into production and deployment while ensuring quality and consistent monitoring. ML model development and deployment are inherently challenging. According to a recent survey, it generally takes anywhere between about 30 to 90 days to push an individual ML model into production, and a year or more on productionizing. Even so, it's estimated that around 90 percent of all ML models fail to even make it to production.