From model creation to deployment: building a predictive maintenance system with streamlit
I am now going to take you through a project involving Predictive Maintenance Recommendation Systems integrated with IoT (Internet of Things) to reduce unplanned downtimes.
The idea is to utilize IoT sensor data from industrial equipment — of course, we’ll be working with fictitious data, but it will simulate what would be real data within a company.
We’ll use this data to create a fully machine-learning-based recommendation system. Along the way, I’ll place a strong emphasis on handling imbalanced data.
I’ll introduce at least 5 different techniques to you. We’ll create five model versions. In the end, we will select the best model, justify our choice, test the model, and then deploy it through a web application using Streamlit.
So, we have quite a bit of work ahead. The link to the complete project on my GitHub will be at the end of this tutorial, along with the bibliography and reference links for you to consult if you wish.