PoC Platform for Comparing Startup ML Models
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Solution Overview
Problem
Conventional proof-of-concept (PoC) platforms lack the ability to efficiently compare and evaluate machine learning (ML) models based on their performance in terms of memory, CPU usage, and ML Key Performance Indicators (KPIs), which are crucial for enterprises to select suitable AI models from startups participating in pilots.
Innovation Solution
A PoC system that includes a networked platform serving enterprise and ISV end-users, featuring a user interface for defining PoCs, a processor to assess ML model suitability, and computation of ML model quality KPIs using stored data and code snippets, allowing for uniform evaluation and comparison of ML models across categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional PoC platforms are used to evaluate software products, then basic software functionality can be tested, but they cannot efficiently compare and evaluate machine learning models based on performance metrics such as memory usage, CPU usage, and ML KPIs
Solution Approach 1:
The PoC platform is enhanced to perform multiple functions: it not only tests basic software functionality but also evaluates machine learning models by computing ML-specific KPIs (memory usage, CPU usage, accuracy, precision, recall, F1 score) alongside traditional software metrics. This multi-functionality allows enterprises to comprehensively assess both software performance and ML model quality within a single unified platform.
2Measurement precision
If ML models from multiple startups are evaluated in detail, then accurate model comparison is achieved, but the time and computational resources required for evaluation increase significantly
Solution Approach 1:
The platform pre-computes and stores ML model KPIs during the evaluation process. By performing preliminary actions such as pre-registering models, pre-defining evaluation criteria, and pre-computing baseline metrics, the platform reduces the time required for actual model comparison when enterprises need to make decisions.
Solution Approach 2:
The platform creates standardized copies of evaluation frameworks and test suites that can be reused across multiple model evaluations. By copying proven evaluation methodologies and pre-configured test scenarios, the platform avoids redundant evaluation work while maintaining consistent and accurate comparison standards across different startups and models.
3Reliability
If comprehensive ML model evaluation is performed, then suitable models can be identified, but the platform requires advanced capabilities that increase its complexity
Solution Approach 1:
The evaluation process is segmented into distinct modular components: software functionality testing, ML model performance evaluation, resource usage measurement, and KPI computation. Each segment handles a specific aspect of evaluation independently, making the overall complex system manageable and maintainable while delivering comprehensive and reliable model assessment capabilities.
Data Source
AI summary
A proof-of-concept (PoC) method comprising: on a networked platform, serving a population of enterprise end-users and a population of ISV end-users, on which PoCs are run, providing a PoC-defining user interface via which at least one enterprise end-user generates a definition of at least one PoC; and using a processor to automatically assess whether an individual machine learning model embodied in a body of code of an individual software product registered for an individual PoC, is suitable for the individual PoC as defined by the definition.