Skip to content
SoccerPredictAI – End-to-End MLOps System
Data Quality
Initializing search
gitlab/dmitry-ivanov-ds/soccer
Start Here
Architecture
Data
ML
Serving
Monitoring
CI/CD
AI-Assisted Development
Evidence
Runbooks
ADR
Reference
SoccerPredictAI – End-to-End MLOps System
gitlab/dmitry-ivanov-ds/soccer
Start Here
Start Here
Review Guide
Implementation Status
Quickstart
Demo Guide
Architecture
Architecture
Principles
System Requirements
System Boundary
System Context (C4 – Level 1)
Container Architecture (C4 – Level 2)
Component Design (C4 – Level 3)
End-to-End Data & ML Flow
Runtime View
Deployment View
Runtime Topology & Environments
Failure Modes
Security & Secrets Management (SOPS + age)
Design Trade-offs
Roadmap
Data
Data
Data Sources & Scraping
Ingestion & Canonicalization
Raw Parquet Export
Canonical Datasets & Lineage
Data Contracts & Quality Gates
Dataset Versioning & Reproducibility
Backfills & Freshness Policy
Data Layer Failure Modes
ML
ML
Problem Formulation & Targets
Baseline & Success Metrics
Validation Strategy & Leakage Prevention
Feature Engineering & Offline/Online Parity
Training Pipeline (DVC)
Hyperparameter Tuning
Experiment Tracking (MLflow)
Model Interface & Signature Contract
Model Registry & Promotion Rules
Limitations & Future Improvements
Serving
Serving
Inference API Contract (FastAPI)
Request / Response Examples
Sync vs Async Inference (Celery/RabbitMQ)
Deployment & Runtime Architecture (Kubernetes/Helm)
Health Checks & Failure Modes
Performance, Capacity & SLOs
Current Serving Status
Monitoring
Monitoring
Service & Infrastructure Metrics (Prometheus)
Dashboards & Visualisation (Grafana)
Data & Model Monitoring (Evidently)
Alerting Strategy
Incident Response & Playbooks
Current Monitoring Coverage
CI/CD
CI/CD
GitLab Pipeline Architecture
Container Build & Registry Strategy
Quality Gates & Release Policy
Testing Strategy
Automated Deployment (Helm)
Release & Rollback Policy
AI-Assisted Development
AI-Assisted Development
Customization Layer (.github/)
Continuous System Audits
Iteration Plans
Evidence
Evidence
System Proofs
MLflow & Training Evidence
API & Serving Evidence
Monitoring Evidence
Evaluation & Reports
Evaluation & Reports
Overview
EDA & Data Profiling
Feature Engineering
Temporal Validation
Model Comparison
Data Quality Gates
Ablation Study
Final Evaluation
Hyperparameter Tuning
Batch Inference
Lessons Learned
Runbooks
Runbooks
Local Development & Debugging
Data Backfills & Reprocessing
Model Retraining
Deployment Recovery
Model Rollback & Recovery
Common Failures & Troubleshooting
On-call Cheat Sheet
ADR
ADR
ADR Template
Decisions
Decisions
ADR-0001 – Pipeline Orchestration
ADR-0002 – Data Versioning Strategy
ADR-0003 – Model Registry & Promotion
ADR-0004 – Secrets Management
ADR-0005 – Serving Modes (Sync vs Async)
Reference
Reference
API (FastAPI)
Pipelines
Configuration (Hydra)
Code Structure
Code Reference
Code Reference
Models
Features
Data
Pipelines (CLI)
Serving
Data Quality
Glossary
Data Quality
¶
Great Expectations suite builders — pure functions, no IO.
Back to top