Summary

This module explores deploying a predictive maintenance algorithm to pumps located remotely in the oil and gas sector. The enterprise captures data from field sensors but wants to deploy predictive maintenance algorithms on edge devices. To address data drift, the enterprise aims to retrain the algorithms automatically.

MLOps lets you manage model development and deployment end to end. You can build, monitor, and validate machine learning models with minimal intervention. These models can be deployed on edge devices like pumps and run offline if needed. Frequent, automatic retraining ensures the most up-to-date model runs on the devices.

Without an MLOps strategy, deployed models might return results that don't reflect the current state of the data. These results can be misleading or incorrect.

Deploying MLOps helps you realize and retain the value of your models by keeping them up to date through retraining. The company can save significantly on maintenance and production costs, improve workplace safety, and reduce its environmental impact.