Implementing machine learning in a low latency environment

A method of utilizing machine learning in a time-constrained environment, comprising:

• obtaining, by one or more processors, sequences of search terms received from client devices over a specified period of time, wherein each search term among the sequences of search terms comprises alphanumeric text;
• converting, by a machine learning model invoked by the one or more processors, the alphanumeric text of each given sequence of search terms, among the sequences of search terms, into a corresponding embedding that differs from the alphanumeric text;
• storing, by the one or more processors, each given sequence of search terms in association with the corresponding embedding of the given sequence of search terms;
• after the obtaining, converting and storing:
• receiving, from a client device, a search term input to a search interface, wherein the search term input includes a most recently input search term;
• matching, by the one or more processors, the search term input to a matching sequence of search terms among the sequences of search terms;
• determining, by the one or more processors, that the corresponding embedding stored in association with the matching sequence of search terms is an inferred embedding of the search term input received from the client device based on the matching of the search term input to the matching sequence of search terms;
• generating, by the one or more processors, at least one predicted next search term predicted to follow the most recently input search term based on the inferred embedding;
• outputting, by the one or more processors to the client device, the at least one predicted next search term to the client device as a recommended completion to the search term input.

A system comprising:

• one or more computers and one or more storage devices storing instructions that, upon execution by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining sequences of search terms received from client devices over a specified period of time, wherein each search term among the sequences of search terms comprises alphanumeric text;
• converting, by a machine learning model invoked by the one or more computers, the alphanumeric text of each given sequence of search terms, among the sequences of search terms, into a corresponding embedding that differs from the alphanumeric text;
• storing each given sequence of search terms in association with the corresponding embedding of the given sequence of search terms;
• after the obtaining, converting and storing:
• receiving, from a client device, a search term input to a search interface, wherein the search term input includes a most recently input search term;
• matching the search term input to a matching sequence of search terms among the sequences of search terms;
• determining that the corresponding embedding stored in association with the matching sequence of search terms is an inferred embedding of the search term input received from the client device based on the matching of the search term input to the matching sequence of search terms;
• generating at least one predicted next search term predicted to follow the most recently input search term based on the inferred embedding;
• outputting, to the client device, the at least one predicted next search term to the client device as a recommended completion to the search term input.

A non-transitory computer-readable medium storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• obtaining sequences of search terms received from client devices over a specified period of time, wherein each search term among the sequences of search terms comprises alphanumeric text;
• converting, by a machine learning model invoked by the one or more computers, the alphanumeric text of each given sequence of search terms, among the sequences of search terms, into a corresponding embedding that differs from the alphanumeric text;
• storing each given sequence of search terms in association with the corresponding embedding of the given sequence of search terms;
• after the obtaining, converting and storing:
• receiving, from a client device, a search term input to a search interface, wherein the search term input includes a most recently input search term;
• matching the search term input to a matching sequence of search terms among the sequences of search terms;
• determining that the corresponding embedding stored in association with the matching sequence of search terms is an inferred embedding of the search term input received from the client device based on the matching of the search term input to the matching sequence of search terms;
• generating at least one predicted next search term predicted to follow the most recently input search term based on the inferred embedding;
• outputting, to the client device, the at least one predicted next search term to the client device as a recommended completion to the search term input.