Summary

Completed

In this module, we considered the scenario of deploying a predictive maintenance algorithm into pumps located remotely (Oil and Gas sector). The enterprise was already capturing data from sensors located in the field – but wanted to deploy predictive maintenance algorithms on edge devices. To overcome data drift, the enterprise wanted to retrain the algorithms automatically.

MLOps enables you to manage the process of model development and deployment end to end. Machine learning models can be built, monitored, and validated with minimal intervention. The models can be deployed on edge devices (pumps) and can run offline if needed. Frequent and automatic retraining of the models ensures that the most up-to-date version of the model is running on the devices.

In the absence of the MLOps strategy, the models deployed may return results that don't reflect the current state of the data. These results may be misleading or even incorrect.

By deploying MLOps, you can realize the value of your models and retain that value over time by keeping the model up to date by retraining. The company can achieve substantial savings on maintenance and production costs and increase workplace safety and their environmental impact with these objectives.