Open AccessExperiencing ProvLake to Manage the Data Lineage of AI Workflows
Author(s) -
Leonardo Guerreiro Azevedo,
Renan Souza,
Raphael Melo Thiago,
Elton Soares,
Márcio Ferreira Moreno
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbsi.2020.13144
Subject(s) - workflow , computer science , process (computing) , domain (mathematical analysis) , core (optical fiber) , data modeling , artificial intelligence , software engineering , database , programming language , mathematical analysis , telecommunications , mathematics
Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.