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SoccerPredictAI – End-to-End MLOps System
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gitlab/dmitry-ivanov-ds/soccer
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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
Data
Table of contents
Parameters
params
Source
source
Preprocessing
preprocess
Splitting
splitting
Storage (DVC / MinIO)
storage
Pipelines (CLI)
Serving
Data Quality
Glossary
Table of contents
Parameters
params
Source
source
Preprocessing
preprocess
Splitting
splitting
Storage (DVC / MinIO)
storage
Data
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Parameters
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Source
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Preprocessing
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Splitting
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Storage (DVC / MinIO)
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