API Recommendation Using Performance Metrics and ML Feedback
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Solution Overview
Problem
Conventional API selection techniques are time-consuming and prone to human error, leading to inefficient API usage and low user conversion rates, particularly in vendor management platforms, as they lack automatic replacement of underperforming APIs.
Innovation Solution
An API recommendation system utilizing a machine learning model that analyzes performance metrics such as response success, system stability, and error rate to dynamically select and replace APIs based on threshold levels, ensuring optimal API performance for subsequent requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual API selection techniques are used, then flexibility and control are maintained, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual API selection (mechanical human decision-making) with an automated machine learning system that analyzes performance metrics and makes API selection decisions automatically, eliminating human error and time consumption while maintaining or improving selection quality
Solution Approach 2:
The system enables self-service by allowing the API selection process to autonomously evaluate performance metrics and make decisions without human intervention, with the machine learning model continuously learning from performance data to improve selections automatically
2Productivity
If conventional API selection methods are used, then implementation simplicity is maintained, but automatic replacement of underperforming APIs is not achieved
Solution Approach 1:
The patent implements a feedback mechanism where performance metrics are continuously collected from API executions, fed into the machine learning model, and used to automatically replace underperforming APIs with better alternatives, creating a closed-loop system that continuously improves productivity
Solution Approach 2:
The system performs preliminary action by pre-evaluating multiple candidate APIs using the machine learning model before actual deployment, selecting the optimal API in advance based on predicted performance, thus avoiding the need for complex real-time switching during execution
3Reliability
If manual API monitoring is performed, then detection capability is maintained, but user conversion rates remain low due to inefficiency
Solution Approach 1:
The patent replaces manual API monitoring with automated machine learning-based performance evaluation that continuously tracks multiple metrics (response time, error rates, throughput) and automatically makes replacement decisions, significantly improving both reliability detection capability and overall system productivity including user conversion rates
Data Source
AI summary
API recommendations based on performance metrics are described. In one or more implementations, an API recommendation system receives a request from a client device. Based on a condition of the request, the API recommendation system selects an application programming interface (API) of a plurality of APIs for performance of the request and stores performance metrics related to the performance of the request by the API in a performance index. The API recommendation system then receives a subsequent request and, using a machine learning model, determines a recommendation on calling the API for performance of the subsequent request by analyzing the performance metrics in the performance index. The API recommendation system then outputs instructions for performing the recommendation on calling the API.