Machine learning model deployment for equipment

An apparatus for data processing, comprising:

• one or more memories storing processor-executable code;
• and
• one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the apparatus to:
• receive, at a cloud platform, a set of real-time data obtained from one or more sensors of a vehicle that comprises a set of components and the one or more sensors measuring one or more metrics of respective components of the set of components of the vehicle;
• generate, an input for a machine learning model, the input comprising the set of real-time data from the one or more sensors of the vehicle;
• generate, via the machine learning model and based on the input comprising the set of real-time data, a prediction score that is associated with a first component of the set of components and is further indicative of a predicted conditional impact on one or more second components of the vehicle that would result from performing versus not performing a maintenance procedure for the first component, wherein the one or more second components are different from the first component;
• and
• transmit, to a computing device for display via a user interface of the computing device, a notification that indicates the maintenance procedure to be performed for the vehicle based at least in part on the prediction score satisfying a prediction score threshold, wherein the prediction score threshold is associated with preventing failure of the one or more second components based on the predicted conditional impact and wherein transmission of the notification occurs prior to the failure of the first component, the one or more second components, or both.

A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by one or more processors to:

• receive, at a cloud platform, a set of real-time data obtained from one or more sensors of a vehicle that comprises a set of components and the one or more sensors measuring one or more metrics of respective components of the set of components of the vehicle;
• generate, an input for a machine learning model, the input comprising the set of real-time data from the one or more sensors of the vehicle;
• generate, via the machine learning model and based on the input comprising the set of real-time data, a prediction score that is associated with a first component of the set of components and is further indicative of a predicted conditional impact on one or more second components of the vehicle that would result from performing versus not performing a maintenance procedure for the first component, wherein the one or more second components are different from the first component;
• and
• transmit, to a computing device for display via a user interface of the computing device, a notification that indicates the maintenance procedure to be performed for the vehicle based at least in part on the prediction score satisfying a prediction score threshold, wherein the prediction score threshold is associated with preventing failure of the one or more second components based on the predicted conditional impact, and wherein transmission of the notification occurs prior to the failure of the first component, the one or more second components, or both.