MLops: The rise of machine learning operations

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.

The lesson here is that new obstacles emerge once machine learning models are deployed to production and used in business processes.

Model management and operations were once challenges for the more advanced data science teams. Now tasks include monitoring production machine learning models for drift, automating the retraining of models, alerting when the drift is significant, and recognizing when models require upgrades. As more organizations invest in machine learning, there is a greater need to build awareness around model management and operations.

The good news is platforms and libraries such as open source MLFlow and DVC, and commercial tools from Alteryx, Databricks, Dataiku, SAS, DataRobot, ModelOp, and others are making model management and operations easier for data science teams. The public cloud providers are also sharing practices such as implementing MLops with Azure Machine Learning.

There are several similarities between model management and devops. Many refer to model management and operations as MLops and define it as the culture, practices, and technologies required to develop and maintain machine learning models.

Copyright © 2020 IDG Communications, Inc.

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