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. Recognizing model drift, retraining models with updating 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.

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.


MLops: The rise of Machine Learning operations
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