Hybrid Cloud Bursting Engine for ML Regulatory Compliance
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Solution Overview
Problem
Cloud bursting for machine learning processes is limited by the lack of regulatory compliant and geographically secure public cloud resources, particularly for sensitive data like medical and financial information, which restricts the use of public clouds due to security and data integrity concerns.
Innovation Solution
Establishing regulatory compliant public cloud resources that adhere to specific national and international regulations, such as HIPAA compliance, and utilizing these resources for computationally intensive machine learning tasks to alleviate the burden on private clouds, while allowing private entities to perform testing and training on private clouds and burst processes to public clouds for improved efficiency and cost reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If public cloud resources are used for machine learning processes, then computational power and cost efficiency are improved, but security and regulatory compliance deteriorate
Solution Approach 1:
The system segments cloud resources into private cloud components (for sensitive data storage and training) and public cloud components (for inference and non-sensitive processing). This segmentation allows the organization to leverage public cloud computational power while maintaining security and compliance for sensitive operations by keeping them in the private cloud.
2Productivity
If sensitive data is moved to public cloud, then resource efficiency is improved, but data security and regulatory compliance deteriorate
Solution Approach 1:
The system applies local quality by differentiating data handling approaches based on sensitivity. Sensitive data remains in the private cloud environment with appropriate security measures, while non-sensitive data can be processed in the public cloud. This allows resource efficiency improvements for non-critical operations without compromising the security of sensitive information.
3Adaptability or versatility
If hybrid cloud architecture is implemented, then flexibility and resource management are improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer (cloud bursting gateway and orchestration mechanisms) that manages communication and data flow between private and public cloud environments. This intermediary abstracts the complexity of hybrid cloud management, providing automated resource allocation, load balancing, and security policy enforcement, thereby improving flexibility without proportionally increasing operational complexity.
Data Source
AI summary
Methods and systems are provided for the deployment of machine learning based processes to public clouds. For example, a method for deploying a machine learning based process may include developing and training the machine learning based process to perform an activity, performing at least one of identifying and receiving an identification of a set of one or more public clouds that comply with a set of regulatory criteria used to regulate the activity, selecting a first public cloud of the set of one or more public clouds that complies with the set of regulatory criteria used to regulate the activity, and deploying the machine learning based process to the first public cloud of the set of one or more public clouds.