17947859. AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT simplified abstract (International Business Machines Corporation)
Contents
- 1 AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED MACHINE LEARNING MODEL DEPLOYMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
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 17947859 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 required code modules in a target execution environment, validating the updated environment for model execution, simulating 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
This technology can be applied in various industries such as healthcare, finance, and e-commerce for optimizing machine learning model deployment processes.
Problems Solved
This technology solves the problem of efficiently managing and deploying machine learning models in different execution environments.
Benefits
The benefits of this technology include improved model deployment efficiency, reduced errors in installation and validation processes, and enhanced simulation capabilities for model execution.
Potential Commercial Applications
"Optimizing Machine Learning Model Deployment Processes in Healthcare"
Possible Prior Art
One possible prior art could be the use of containerization technologies like Docker for deploying machine learning models in different environments.
Unanswered Questions
How does this technology handle updates to the machine learning model itself?
The abstract does not mention how updates to the machine learning model are managed and deployed in the target execution environment.
What security measures are in place to protect the deployed machine learning model?
The abstract does not provide information on the security measures implemented to safeguard the deployed machine learning model from potential threats.
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.
