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The rise of MLOps

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Developing machine learning (ML) models are a taunting task for data scientists, however, managing these models in production can be even harder. In order to have successful results, data scientists need to recognize the model drift, retrain the model with updated data sets, improve performance, and maintain the underlying technology platforms. Hence, developing production-ready models are something difficult and long to achieve.

New challenges always appear once ML models are deployed to production and used within the business processes. With more organizations adopting ML, there is a need to be aware of model management and operations. This is where MLOps – Machine Learning Operations – comes into play to make model management and operations easier and faster...

The post The rise of MLOps appeared first on DevOps Online.

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