ML Recommendation System for Cloud Release Adoption
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Solution Overview
Problem
Enterprise customers face challenges in adapting to and adopting new releases from cloud providers due to the rapid pace of changes, leading to wastage of human and computing resources in manually reviewing information, and often fail to identify relevant new releases.
Innovation Solution
A recommendation system that processes release data, customer data, and interest data using machine learning models to filter, classify, and match relevant new releases for customers, providing a unified view and conserving resources by automating the adoption of relevant updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of release information is performed, then customers can identify relevant new releases, but human and computing resources are wasted
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based recommendation system. The system uses trained models to process release data, customer data, and interest data, automatically generating recommendations without human intervention. This substitution eliminates resource wastage while maintaining identification accuracy through algorithmic processing.
Solution Approach 2:
The recommendation system enables customers to automatically receive personalized release recommendations based on their own usage patterns, inventory, and billing data. The system self-adjusts recommendations by processing customer-specific data without requiring manual input or review, allowing customers to benefit from automated resource-efficient release identification.
2Loss of information
If manual review of all release information is performed, then comprehensive coverage is achieved, but time consumption increases
Solution Approach 1:
The system extracts only the most relevant release information for each customer based on their specific context. The recommendation models process all release data but extract and present only the subset that is personally relevant to each customer. This extraction approach maintains comprehensive information coverage while dramatically reducing the time customers need to spend reviewing releases.
Solution Approach 2:
The system performs partial action by generating personalized recommendations that cover only the necessary portion of release information for each customer. Rather than requiring customers to review all releases comprehensively, the system provides targeted partial coverage that is sufficient for informed decision-making, thereby reducing time consumption while maintaining adequate information coverage.
3Adaptability or versatility
If personalized recommendations are generated for each customer, then relevance is improved, but system complexity increases
Solution Approach 1:
The recommendation system uses universal machine learning models that can handle multiple data types (release data, customer data, interest data) and generate personalized recommendations for all customers through a single unified system. This multi-functional approach enables personalization for diverse customer needs without requiring separate specialized systems for each customer type, thereby managing complexity while maintaining adaptability.
Solution Approach 2:
The system achieves personalization by changing input parameters (customer usage patterns, inventory levels, billing information, interests) rather than changing the core recommendation engine. The universal model adapts to individual customers by processing different parameter combinations, enabling high adaptability without increasing fundamental system complexity. The model structure remains consistent while parameter variations drive personalization.
Data Source
AI summary
In some implementations, a device may receive release data identifying new releases associated with cloud providers. The device may receive customer data associated with customers of the cloud providers. The device may receive interest data identifying interests of the customers. The device may filter the release data and may process the filtered release data and taxonomy data identifying historical release data, with a model, to generate classification data identifying classifications of the release data. The device may process the filtered release data, the classification data, and the customer data, with a model, to identify a set of release data that is relevant for each customer, and to generate sets of release data for the customers. The device may identify additional data of the filtered release data that is relevant for each of the customers. The device may supplement the sets of release data with the additional data.