International business machines corporation (20240095001). AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT simplified abstract

AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT

Organization Name

international business machines corporation

Inventor(s)

Dhavalkumar C. Patel of White Plains NY (US)

AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240095001 titled 'AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT

Simplified Explanation

The abstract of the patent application describes a method for identifying resource usage specifications and code module installation specifications for a machine learning model, installing the necessary code modules in a target execution environment, validating the updated environment for model execution, simulating the model execution, and deploying the model in the updated environment.

• Identification of resource usage specifications and code module installation specifications for a machine learning model
• Installation of required code modules in a target execution environment
• Validation of the updated environment for model execution
• Simulation of model execution in the updated environment
• Deployment of the model in the updated environment

Potential Applications

The technology described in this patent application could be applied in various industries where machine learning models are deployed and updated regularly, such as healthcare, finance, and e-commerce.

Problems Solved

This technology streamlines the process of updating and deploying machine learning models by automating the identification of resource and code module requirements, thus reducing the risk of errors and improving efficiency.

Benefits

The benefits of this technology include faster deployment of updated machine learning models, improved accuracy in identifying resource and code module requirements, and overall increased efficiency in the model deployment process.

Potential Commercial Applications

• Automated machine learning model deployment software for businesses
• Cloud computing services for machine learning model deployment

Possible Prior Art

One possible prior art could be the use of containerization technologies like Docker to streamline the deployment of machine learning models in different environments.

How does this technology handle security concerns during the deployment process?

The patent abstract does not mention how security concerns are addressed during the deployment of machine learning models. This could be a crucial aspect to consider, especially in sensitive industries like healthcare and finance.

What measures are in place to ensure the compatibility of the code modules with the target execution environment?

The abstract does not provide details on how the compatibility of the code modules with the target execution environment is ensured. It would be important to understand the mechanisms in place to prevent conflicts or errors during the installation process.

Original Abstract Submitted

using exported data of a machine learning model and a model training environment specification, a resource usage specification and a code module usage specification of the model are identified. a code module installation specification is determined from a code module requirements specification and a target execution environment specification. the code modules specified by the code module installation specification are caused to be installed in the target execution environment. using data of the updated target execution environment, the updated target execution environment is validated for execution of the model. execution of the model in the updated target execution environment is simulated. the model is deployed in the updated target execution environment responsive to the simulating being successful.