OPEN TEXT HOLDINGS, INC. (20240330763). SYSTEMS AND METHODS FOR API-BASED MACHINE LEARNING MODEL PUBLICATION simplified abstract
Contents
SYSTEMS AND METHODS FOR API-BASED MACHINE LEARNING MODEL PUBLICATION
Organization Name
Inventor(s)
Abhishek Pattanaik of Hyderabad (IN)
Thirumalesh Yenagandula of Hyderabad (IN)
Mahima Khatri of Puppalaguda (IN)
SYSTEMS AND METHODS FOR API-BASED MACHINE LEARNING MODEL PUBLICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330763 titled 'SYSTEMS AND METHODS FOR API-BASED MACHINE LEARNING MODEL PUBLICATION
The abstract describes a system for format-agnostic publication of machine learning models through an API, allowing models from different computing environments to be validated, normalized, and published as Docker images to a registry for deployment on managed clusters.
- Machine learning models can be received and processed through an API.
- The system validates and normalizes the models for consistency.
- Docker images are generated for each validated model.
- The Docker images are published to a registry for deployment.
- Models can be deployed on managed clusters in enterprise or cloud environments.
Potential Applications: This technology can be used in various industries such as healthcare, finance, and e-commerce for deploying machine learning models on managed clusters for real-time decision-making and predictive analytics.
Problems Solved: The system addresses the challenge of deploying machine learning models developed in different environments to managed clusters seamlessly and efficiently.
Benefits: The system streamlines the deployment process of machine learning models, ensuring consistency and compatibility across different computing environments, leading to faster implementation and improved scalability.
Commercial Applications: Title: "Efficient Deployment of Machine Learning Models on Managed Clusters" This technology can be commercially applied in industries requiring real-time analytics and decision-making, enhancing operational efficiency and enabling data-driven insights for better business outcomes.
Prior Art: Readers can explore existing research on machine learning model deployment systems, API integration, and Docker image generation for further insights into related technologies.
Frequently Updated Research: Stay updated on advancements in machine learning model deployment, API technologies, and containerization methods for continuous improvement in deploying analytics models effectively.
Questions about Machine Learning Model Publication: 1. How does the system ensure the compatibility of machine learning models from different computing environments? 2. What are the key advantages of using Docker images for deploying machine learning models on managed clusters?
Original Abstract Submitted
a system for format-agnostic publication of a machine learning model may receive, through an application programming interface (api), machine learning models that have been built, developed, and trained in disparate computing environments, validate and normalize these machine learning models, generate a docker image for each validated and standardized machine learning model, and publish the docker images to a docker registry. the docker images can then be deployed to a managed cluster such as an on-prem managed cluster operating in an enterprise computing environment and/or a managed hyperscale cluster operating in a cloud computing environment. this api-based machine learning model publication approach allows any analytics model developed and trained in any modeling environment be deployed to any managed cluster.
