Toggle navigation sidebar

Toggle in-page Table of Contents

AI Supported Continuous Integration

AI4CI : AI for Continuous Integration

Getting started

Get Started
Contribute to the AI for Continuous Integration Project
Table of Contents

Failure type classifier

Failure type classification with the TestGrid data
Flake Detection for TestGrid
Failure type classification
Failure type functions

Time to merge prediction

Time to Merge Prediction

Build Log Classifier

openshift-ci: build logs initial data collection and EDA
Term frequency analysis of build logs
Seldon deployment for build log clustering
Prow Logs and GCS Data

TestGrid data

TestGrid: initial EDA and data collection
TestGrid In-Depth EDA
TestGrid: exploring in-depth metadata
TestGrid Additional Features - uniform or unique?
KPI Metrics

Bugzilla Data

Bugzilla Data for CI Tests on Testgrid

Sippy data

Sippy Export of OpenShift Data - EDA
SIPPY
Initial EDA

Telemetry data

Telemetry Data for CI Clusters

Workshop

Pre-requisites
Introduction
Onboarding
Set up Github environment
Model Development
Create an ML Pipeline using Elyra and Kubeflow Pipelines
Model Deployment
SQL Query Engine - Trino
Visualization Dashboard - Superset

Powered by Jupyter Book

repository
open issue

.md

SQL Query Engine - Trino

SQL Query Engine - Trino#

This section will include instructions for sending the data via Trino notebook, accessing and interacting with the dataset through Cloudbeaver UI.

previous

Model Deployment

next

Visualization Dashboard - Superset

By AIOps
© Copyright 2022.