As hard as it is for data scientists to tag data and develop accurate machine learning models, managing models in production can be even more daunting.
Spotting model drift, retraining models with updated data sets, improving performance, and maintaining the underlying technology platforms are all important data science practices.
Without these disciplines, models can produce erroneous results that significantly impact business. The lesson here is that new obstacles emerge once machine learning models are deployed to production and used in business processes.
Developing production-ready models is no easy feat. According to one machine learning study, 55 percent of companies had not deployed models into production, and 40 percent or more require more than 30 days to deploy one model. Success brings new challenges, and 41 percent of respondents acknowledge the difficulty of versioning machine learning models and reproducibility.