Loyiha 4: End-to-End MLOps Pipeline
🎯 Maqsad
**Sizning eng muhim portfolio loyihangiz.**To'liq end-to-end MLOps platform — barcha o'rgangan tool'larni birlashtirgan production-grade ML system. Bu loyiha sizning ML Engineer / MLOps Engineersifatidagi tayyorgarligingizning eng yaxshi isboti.
Use case (tanlash)
Avvalgi 3 loyihangizdan birini MLOps lensorqali qayta qurish — eng yaxshi yondashuv.
| Variant | Murakkablik |
|---|---|
| Klassik ML loyihasini MLOps'lash (Loyiha 1 ni asos qiling) | ⭐⭐⭐⭐ |
| CV system + MLOps (Loyiha 2 ni asos qiling) | ⭐⭐⭐⭐⭐ |
| LLM Pipeline + LLMOps (Loyiha 3 ni asos qiling) | ⭐⭐⭐⭐⭐ |
| Yangi loyiha (boshidan) | ⭐⭐⭐⭐⭐ |
**Tavsiya:**Loyiha 1'ni asos qiling — fokus MLOps'da, ML qism oddiy bo'lsa ham bo'ladi.
To'liq Architecture
┌─────────────────────────────────────────────────────────────────┐
│ SOURCE LAYER │
│ Git (GitHub) + DVC (S3/MinIO) + Notion/Confluence │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ DATA PIPELINE (Airflow) │
│ Extract → Validate → Transform → Feature Store │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ TRAINING PIPELINE │
│ DVC repro → MLflow tracking → Hyperparameter tuning │
│ → Validation → Model Registry → A/B Decision │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ CI/CD PIPELINE │
│ GitHub Actions → Code tests → Model tests → Build → Deploy │
│ → Canary → Production │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ SERVING LAYER │
│ FastAPI + ONNX + Redis cache → K8s (HPA) → Ingress │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ MONITORING LAYER │
│ Prometheus → Grafana │
│ Evidently AI → Drift Alerts → Auto-retrain trigger │
│ Loki → Centralized logging │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ OBSERVABILITY │
│ Sentry (errors) → Slack (alerts) → Statuspage (uptime) │
└─────────────────────────────────────────────────────────────────┘
Tech Stack (full)
Core
- **Code:**Python 3.11+, FastAPI, SQLAlchemy
- **ML:**scikit-learn, XGBoost (yoki PyTorch)
- **Container:**Docker, Docker Compose
- **Orchestration:**Kubernetes (minikube yoki real)
MLOps tools
- **Experiment tracking:**MLflow
- **Data versioning:**DVC + S3/MinIO
- **Workflow orchestration:**Apache Airflow
- **Model serving:**FastAPI + ONNX (yoki BentoML)
- **Feature store:**Feast (bonus)
Monitoring
- **Metrics:**Prometheus + Grafana
- **Drift detection:**Evidently AI
- **Logging:**Loki yoki ELK
- **Errors:**Sentry
- **Alerts:**AlertManager + Slack
CI/CD
- **Source:**GitHub
- **Pipeline:**GitHub Actions
- **CML:**Continuous ML reports
- **Helm:**Kubernetes packaging
Features (to'liq ro'yxat)
Foundation (1-hafta)
-
Project structure (
cookiecutter-data-science) - DVC + remote storage (S3/MinIO)
- MLflow Server (Docker)
- Initial data pipeline
- Baseline model + MLflow logging
Training Pipeline (2-hafta)
-
DVC pipeline (
dvc.yaml) - Hyperparameter tuning (Optuna + MLflow)
- Model validation tests
- Model Registry workflow (Staging → Production)
- Sintetik data validation
Serving (3-hafta)
- FastAPI production-ready
- ONNX export va inference
- Async batching
- Multi-model serving
- A/B test infrastructure
- Health checks, Prometheus metrics
Deployment (3-hafta)
- Multi-stage Dockerfile
- docker-compose (full stack)
- Kubernetes manifests
- Helm chart
- HPA + resource limits
- Blue-green yoki canary
Monitoring (4-hafta)
- Prometheus metrics
- Grafana dashboards (3+ dashboard)
- Evidently daily drift reports
- AlertManager rules + Slack
- Centralized logging
- Sentry integration
CI/CD (4-hafta)
- GitHub Actions: code tests
- Data tests (DVC + Great Expectations)
- Model tests (accuracy, robustness)
- CML reports on PR
- Auto-deploy on merge to main
- Manual approval for production
Continuous Training
- Airflow DAG: weekly retraining
- Drift-triggered retraining
- Auto-deployment if better
- Rollback if worse
Final project structure
mlops-platform/
├── README.md # Comprehensive
├── ARCHITECTURE.md # System design
├── CONTRIBUTING.md
├── docker-compose.yml # Full stack local
├── Dockerfile.api
├── Dockerfile.training
├── Dockerfile.airflow
├── Makefile # All common commands
├── pyproject.toml
├── requirements.txt
├── .env.example
│
├── src/
│ ├── api/ # FastAPI serving
│ ├── data/ # Data pipelines
│ ├── features/ # Feature engineering
│ ├── models/ # Training, eval
│ ├── monitoring/ # Drift, metrics
│ └── utils/
│
├── tests/
│ ├── unit/
│ ├── data/ # Data validation
│ ├── model/ # Model validation
│ ├── integration/
│ └── e2e/
│
├── dvc.yaml # Pipeline
├── params.yaml # Hyperparams
├── dvc.lock
│
├── airflow/
│ ├── dags/
│ │ ├── retrain_dag.py
│ │ ├── inference_dag.py
│ │ └── monitoring_dag.py
│ ├── plugins/
│ └── docker-compose-airflow.yml
│
├── k8s/ # OR helm/
│ ├── deployment.yaml
│ ├── service.yaml
│ ├── ingress.yaml
│ ├── hpa.yaml
│ ├── configmap.yaml
│ ├── secret-template.yaml
│ └── kustomization.yaml
│
├── helm/ # Optional
│ └── mlops-platform/
│ ├── Chart.yaml
│ ├── values.yaml
│ └── templates/
│
├── monitoring/
│ ├── prometheus/
│ │ └── prometheus.yml
│ ├── alertmanager/
│ │ └── alerts.yml
│ ├── grafana/
│ │ └── dashboards/
│ └── evidently/
│ └── monitoring_config.py
│
├── .github/workflows/
│ ├── ci.yml
│ ├── ml-pipeline.yml
│ ├── deploy-staging.yml
│ ├── deploy-production.yml
│ └── rollback.yml
│
├── notebooks/ # Exploration
├── data/ # DVC tracked
│ ├── raw/
│ ├── interim/
│ └── processed/
├── models/ # Local copies
├── reports/ # MLflow + Evidently outputs
├── docs/ # mkdocs
└── scripts/ # Utility scripts
Implementatsiya plani (4 hafta)
Hafta 1 — Foundation
- Day 1: Project structure, repo setup
- Day 2: DVC + MinIO local
- Day 3: MLflow Docker setup
- Day 4: Initial data pipeline (Python)
- Day 5: Baseline model + MLflow tracking
- Day 6: Tests + GitHub
- Day 7: README first draft
Hafta 2 — Training + CI
- Day 8: DVC pipeline (
dvc.yaml) - Day 9: Hyperparameter tuning (Optuna)
- Day 10: Model validation tests
- Day 11: Model Registry workflow
- Day 12: GitHub Actions CI
- Day 13: CML reports
- Day 14: Documentation
Hafta 3 — Serving + Deployment
- Day 15: FastAPI production
- Day 16: ONNX optimization
- Day 17: Docker Compose full stack
- Day 18: Kubernetes manifests
- Day 19: minikube deployment
- Day 20: HPA + load testing
- Day 21: A/B test infrastructure
Hafta 4 — Monitoring + Continuous Training
- Day 22: Prometheus + Grafana
- Day 23: Evidently drift reports
- Day 24: AlertManager + Slack
- Day 25: Airflow DAGs (retraining)
- Day 26: End-to-end testing
- Day 27: Cloud deployment (optional)
- Day 28: Demo video + blog post + LinkedIn
Success metrics
Technical
- **All tests pass:**Code, data, model
- **Deployment:**Working K8s deployment
- **Monitoring:**All 4 dashboards live
- **CI/CD:**Green on main branch
- **Continuous training:**Weekly Airflow DAG running
Documentation
- **README:**Comprehensive, with diagrams
- **Architecture doc:**Decisions explained
- **API docs:**OpenAPI auto-generated
- **Runbook:**Incident response procedures
Production readiness
- Latency p95:< 100ms
- **Throughput:**1000+ RPS
- Uptime:> 99% (load tested)
- **Cost optimization:**Documented
Resurslar
- MLOps Zoomcamp — github.com/DataTalksClub/mlops-zoomcamp — MUST DOkurs
- Made With ML — madewithml.com — production patterns
- "Designing ML Systems" — Chip Huyen
- "ML Engineering" — Andriy Burkov
- Awesome MLOps — GitHub list
Bonus features (extra credit)
- Multi-model platform — bir nechta model bitta system'da
- Feature Store — Feast integration
- Real-time streaming — Kafka + Flink
- Multi-cloud — AWS + GCP
- Cost dashboard — per model spend
- User management — multi-tenant
- API gateway — Kong yoki Tyk
- Service mesh — Istio
✅ Submission checklist
- GitHub repo (public, clean)
- Comprehensive README (badges, diagrams, examples)
- Architecture diagram (Mermaid + slides)
- All tests passing (badges)
-
Docker Compose works (
make up) - K8s deployment works
- All 4 monitoring dashboards (screenshots in README)
- Airflow DAG running (screenshot)
- MLflow Registry (screenshot)
- CML reports on PRs
- Demo video (10-20 min)
- Architecture blog post
- LinkedIn post (with all links)
- CV updated
- Job applications sent!
Bu loyihadan keyin
Siz endi quyidagilarni dadil aytasiz:
✅ "I built an end-to-end MLOps platform that..." ✅ "I have experience with MLflow, DVC, Airflow, Kubernetes for ML..." ✅ "I implemented drift detection and automated retraining..." ✅ "I designed CI/CD pipelines for ML with model validation..."
Bular MLOps Engineervakansiyalari uchun interviewlarda asosiy savollar — siz ham javob bera olasiz, ham real loyiha bilan ko'rsata olasiz.
Tabriklayman!
Agar bu 4 ta loyihani tugatsangiz, siz ML Engineer / MLOps Engineersifatida xalqaro vakansiyalarga ham ariza yubora olasiz.
Keyingi qadam:
- CV yangilash — bu loyihalar bilan
- LinkedIn optimization — title: "ML Engineer | MLOps | Python"
- Job applications — 20+ vakansiya
- Mock interviews — Pramp, Interviewing.io
- Open source contributions — MLflow, Airflow, DVC, Evidently'ga
- Public speaking — meetup'larda gapirish
- Mentorship — boshqalarga o'rgatish
Sizning yo'lingiz endi ochiq. Omad!