From mannequin creation to deployment: constructing a predictive upkeep system with streamlit
I’m now going to take you thru a challenge involving Predictive Upkeep Suggestion Methods built-in with IoT (Web of Issues) to scale back unplanned downtimes.
The thought is to make the most of IoT sensor information from industrial tools — in fact, we’ll be working with fictitious information, however it is going to simulate what can be actual information inside an organization.
We’ll use this information to create a completely machine-learning-based suggestion system. Alongside the best way, I’ll place a powerful emphasis on dealing with imbalanced information.
I’ll introduce at the least 5 totally different methods to you. We’ll create 5 mannequin variations. Ultimately, we’ll choose the perfect mannequin, justify our alternative, check the mannequin, after which deploy it by an online utility utilizing Streamlit.
So, we have now fairly a bit of labor forward. The hyperlink to the challenge on my GitHub will likely be on the finish of this tutorial, together with the bibliography and reference hyperlinks so that you can seek the advice of if you want.
Let me know if this adjustment meets your expectations or if there’s the rest you’d like to change!