LARGE LANGUAGE MODEL DEPLOYMENT

1 . A computer system for deploying one or more large language models, the computer system comprising:

one or more processors; and

non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to:

manage deployment of one or more machine learning models;

generate model configuration files, wherein the model configuration files implement the one or more machine learning models in one or more environments and provide a specification library used to configure the one or more machine learning models;

determine scores of a performance of the one or more machine learning models in the one or more environments; and

store the model configuration files that are used to deploy each corresponding machine learning model.

2 . The computer system of claim 1 , wherein the instructions further cause the computer system to maintain the one or more machine learning model on-premises or in a cloud instance.

3 . The computer system of claim 2 , wherein the one or more machine learning models are accessed through a RESTful endpoint.

4 . The computer system of claim 3 , wherein an external device accesses the one or more machine learning models through the RESTful endpoint.

5 . The computer system of claim 1 , wherein the instructions further cause the computer system to monitor real-time scoring data of the one or more machine learning models.

6 . The computer system of claim 5 , wherein the event streaming device stores the scoring data in a database.

7 . The computer system of claim 6 , wherein the database is a relational database or a non-relational database.

8 . The computer system of claim 1 , wherein the instructions further cause the computer system to update a model configuration file to update the one or more machine learning models.

9 . The computer system of claim 8 , wherein updating the one or more machine learning models are completed without updating underlying code of the one or more machine learning models.

10 . The computer system of claim 9 , wherein updating the model configuration file is further programmed to generate a new model configuration file based on a machine learning model template.

11 . A method for deploying one or more machine learning models, the method comprising:

developing a machine learning model for one or more use cases;

operationalizing the machine learning model for the one or more use cases;

deploying the machine learning model to one or more client devices; and

operating the machine learning model on the one or more client devices.

12 . The method of claim 11 , further comprising generating one or more model configuration files corresponding to the one or more machine learning models.

13 . The method of claim 12 , wherein the machine learning model is developed from the one or more configuration files.

14 . The method of claim 13 , wherein each of the one or more configuration files correspond to at least one of the one or more-use cases.

15 . The method of claim 11 , further comprising scoring the one or more machine learning models for the one or more use cases.

16 . The method of claim 15 , further comprising storing scoring data in a database.

17 . The method of claim 16 , wherein the database is a relational database or a non-relational database.

18 . The method of claim 11 , further comprising determining an instance type for deploying the machine learning model.

19 . The method of claim 18 , wherein the instance type is on-premises or a cloud instance.

20 . The method of claim 18 , further comprising accessing the machine learning model through a Representational State Transfer (“RESTful”) API.