-
FrugalMCT: Efficient Online ML API Selection for Multi-Label Classification Tasks
Multi-label classification tasks such as OCR and multi-object recognitio...
read it
-
Blockchain Enabled Trustless API Marketplace
There has been an unprecedented surge in the number of service providers...
read it
-
Enterprise API Security and GDPR Compliance: Design and Implementation Perspective
With the advancements in the enterprise-level business development, the ...
read it
-
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis
Understanding expressed sentiment and emotions are two crucial factors i...
read it
-
A Web Scraping Methodology for Bypassing Twitter API Restrictions
Retrieving information from social networks is the first and primordial ...
read it
-
Security Analysis of the Open Banking Account and Transaction API Protocol
To counteract the lack of competition and innovation in the financial se...
read it
-
Collecting Service-Based Maintainability Metrics from RESTful API Descriptions: Static Analysis and Threshold Derivation
While many maintainability metrics have been explicitly designed for ser...
read it
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply
Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90 accuracy of the best single API, or up to 5 best API's cost.
READ FULL TEXT
Comments
There are no comments yet.