Implementation Status¶

This page provides an honest assessment of what's implemented, in progress, and planned.

Last updated: February 27, 2026

Implementation Matrix¶

Legend¶

• ✅ Operational: Implemented, tested, and working in practice
• 🚧 In Progress: Partial implementation or active development
• 📋 Planned: Designed but not yet implemented

Known Limitations¶

Current Limitations¶

1. Inference Layer
2. ✅ Sync and async POST /predict implemented with MLflow Registry integration
3. ✅ Async inference via Celery ml queue with task status polling
4. Model promotion (Staging→Production) requires manual approval — no automated policy

5. No HTTP batch prediction endpoint (batch features computed by DVC pipeline only)

6. Testing

7. ✅ ~200 tests: unit, property (Hypothesis), service, contract, load (Locust)
8. ✅ No-leakage invariant verified for rolling features

9. Integration tests use mocks — no live Celery/MLflow dependency in CI

10. Monitoring

11. ✅ Prometheus /metrics endpoint operational, 8 metrics exported
12. ✅ Request latency and prediction counters instrumented
13. Grafana dashboards not yet deployed
14. Evidently drift detection not yet integrated

15. Alerting rules documented but not deployed

16. Data Quality

17. ✅ Great Expectations suites run as mandatory DVC pipeline stages
18. No scheduled GE data docs refresh or CI-blocking quality gates yet

Architectural Debt¶

The following represent deliberate trade-offs made for MVP:

• Feature Store: Currently file-based (Parquet); migration to dedicated store in roadmap
• Config Management: params.yaml + DVC params; Hydra in roadmap for multi-env configs
• Model Promotion: Registration automated; Staging→Production gate is manual
• Drift Detection: Evidently wired into roadmap but not yet integrated
• Real-time Monitoring: Prometheus metrics live; Grafana dashboards and alerting pending

What Works End-to-End¶

✅ Reproducible Training Pipeline¶

dvc pull        # Get versioned datasets
dvc repro       # Run full pipeline
mlflow ui       # Inspect experiments

Result: Deterministic model training with tracked experiments.

✅ Data Ingestion¶

# Airflow DAGs running
# PostgreSQL populated
# MinIO artifacts stored

Result: Automated data updates from web sources.

✅ Infrastructure¶

docker build    # Multi-stage images
kubectl apply   # K8s deployment
helm install    # Parameterized configs

Result: Deployment-ready infrastructure.

What Doesn't Work Yet¶

❌ Drift Monitoring¶

# No Evidently reports generated
# No drift alerts triggered

Reason: Evidently integration designed but not yet implemented.

❌ Grafana Dashboards¶

# Prometheus scraping works, dashboards not deployed

Reason: Infrastructure ready; dashboard provisioning not automated.

Next Milestones¶

Current: Monitoring & Operations¶

Goal: Full observability layer

• [ ] Grafana dashboard (latency, throughput, error rate)
• [ ] Evidently drift detection integrated
• [ ] Alerting rules deployed (Prometheus Alertmanager)
• [ ] Automated model promotion policy

Success Criteria: Observable system with automated quality gates

Next: MLOps Maturity¶

Goal: Reduce operational toil

• [ ] Hydra multi-environment config management
• [ ] Feast feature store migration (replace file-based parquet)
• [ ] A/B testing infrastructure
• [ ] Load testing benchmarks validated

Success Criteria: Reproducible, configurable, and scalable ML system

How to Verify Claims¶

Data Pipeline¶

# Check DVC tracking
dvc status

# Verify dataset versions
cat data/raw/match.parquet.dvc

# Check Airflow DAGs
ls airflow/dags/

ML Training¶

# Run pipeline
dvc repro

# Check MLflow
mlflow ui --port 5001
# Navigate to experiments tab

Infrastructure¶

# Check Docker images
docker images | grep soccer

# Check K8s manifests
ls k8s/manifests/

# Check CI pipeline
cat .gitlab-ci.yml | grep stages

Serving¶

# Start API + ML worker
uvicorn src.app.main:app

# Check healthcheck
curl http://localhost:8000/healthcheck/

# List upcoming matches
curl http://localhost:8000/predict/matches/

# Sync prediction by match ID
curl http://localhost:8000/predict/42

# Async prediction
curl -X POST http://localhost:8000/predict/async/ -H 'Content-Type: application/json' \
  -d '{"match_id": 42}'

# Check metrics
curl http://localhost:8000/metrics

Honest Assessment for Interviews¶

What to say:

"This is a portfolio project demonstrating a production-style MLOps lifecycle: data ingestion via Airflow, versioned datasets and reproducible pipelines via DVC, experiment tracking and model registry via MLflow, sync and async inference via FastAPI + Celery, Prometheus metrics, and CI/CD with GitLab. The full stack is Dockerized with K8s/Helm manifests."

What NOT to say:

❌ "Drift detection and Grafana dashboards are fully operational in production" ❌ "The model is production-calibrated and profitable"

What to highlight:

✅ "End-to-end pipeline: scraping → features → training → serving → monitoring" ✅ "Both sync (POST /predict) and async (POST /predict/async/) inference implemented" ✅ "DVC + MLflow ensure reproducibility and experiment traceability" ✅ "~200 tests: unit, property (Hypothesis), service, contract, and load (Locust)" ✅ "GitLab CI with SOPS secrets, Docker multi-stage builds, Helm-based K8s deploy"

Trade-offs & Learnings¶

Why Some Things Aren't Done¶

1. Inference endpoint delay: Focused on getting the ML pipeline reproducible first (train before serve)
2. Monitoring gaps: Infrastructure designed, integration deferred to avoid premature optimization
3. Test coverage: Framework setup complete, expanding coverage systematically
4. Model Registry manual: Automated promotion requires production traffic patterns to validate

What I'd Do Differently¶

• Start with simpler deployment (FastAPI only, add workers later)
• Write tests alongside features, not after
• Implement basic monitoring earlier for feedback loop
• Use feature flags for gradual rollout

References¶

• Architecture Documentation - System design and C4 diagrams
• ADRs - Architectural decision records
• Roadmap - Planned features and phases
• DEMO.md - Live demonstration guide