Abstract: An approach to generating end-to-end visualizations of invocations from coarse granular application programming interface (API) requests within a containerized environment may be presented. A coarse-granular API request may be intercepted. The coarse-granular API request may receive a unique identifier, which will be assigned to all invocations associated with the coarse-granular API request. Any invocations associated with the coarse-granular API within the containerized environment may be monitored. Detected invocations resulting from the coarse-granular API request may be annotated with a sequence number and the unique ID of the associated coarse-granular API request. An invocation flow for the coarse-granular API request may be generated based on the unique ID, relationship between the invocations and microservices, and the sequence number of the invocations.

Abstract: A system, platform, program product, and/or method for protecting sensitive data including decrypting an incoming message comprising a base message and the sensitive electronic data; removing the sensitive electronic data from the incoming message to create a stripped message; encrypting the sensitive electronic data; storing the encrypted sensitive electronic data in In-Memory Cache; and permitting the stripped message to be further processed without the sensitive electronic data. The system, platform, program product and/or method in an embodiment further includes: retrieving from the In-Memory Cache the encrypted sensitive electronic data; decrypting the encrypted sensitive electronic data retrieved from the In-Memory Cache; and injecting the sensitive electronic data into the stripped message.

Abstract: In an approach for constructing private profile machine learning models for recommending products to a user, a processor gathers user data associated with interactions of the user on an ecommerce website. A processor analyzes the user data using machine learning (ML) techniques. A processor trains a private profile ML model on the analyzed user data, wherein the private profile ML model is stored on a private storage of the user. A processor predicts a product recommendation using the private profile ML model. A processor outputs the product recommendation.

Abstract: An approach to generating end-to-end visualizations of invocations from coarse granular application programming interface (API) requests within a containerized environment may be presented. A coarse-granular API request may be intercepted. The coarse-granular API request may receive a unique identifier, which will be assigned to all invocations associated with the coarse-granular API request. Any invocations associated with the coarse-granular API within the containerized environment may be monitored. Detected invocations resulting from the coarse-granular API request may be annotated with a sequence number and the unique ID of the associated coarse-granular API request. An invocation flow for the coarse-granular API request may be generated based on the unique ID, relationship between the invocations and microservices, and the sequence number of the invocations.

Abstract: In an approach for constructing private profile machine learning models for recommending products to a user, a processor gathers user data associated with interactions of the user on an ecommerce website. A processor analyzes the user data using machine learning (ML) techniques. A processor trains a private profile ML model on the analyzed user data, wherein the private profile ML model is stored on a private storage of the user. A processor predicts a product recommendation using the private profile ML model. A processor outputs the product recommendation.