Final Loyihalar (Portfolio)

🎯 Maqsad

6 oy davomida o'rgangan bilimlaringizni amaliyotda ko'rsatadigan 4 ta katta loyiha. Bular sizning:

• GitHub portfoliongiz
• CV'dagi "Projects" bo'limi
• Interviewlar uchun materialingiz
• LinkedIn postlaringiz

4 ta loyiha

Har bir loyiha uchun talablar (minimum)

Texnik

• GitHub'da public repo(clear README)
• Docker + docker-compose — bir buyruq bilan ishga tushadigan
• Tests — pytest, kamida 50% coverage
• CI/CD — GitHub Actions
• API documentation — OpenAPI/Swagger
• Architecture diagram(Mermaid yoki Excalidraw)
• Environment variables — .env.example faylda

Code Quality

• Type hints — Pythonda hamma yerda
• Linting — ruff yoki flake8
• Formatting — black yoki ruff format
• Pre-commit hooks

Documentation

• README — installation, usage, API examples
• Architecture explanation — qaror sabablari
• Demo video — Loom (5-10 daqiqa)
• Blog post — Medium/dev.to (har biri uchun)

Production

• Healthcheck endpoint — /health
• Logging — structured (JSON)
• Error handling — Sentry yoki shunga o'xshash
• Rate limiting — slowapi yoki nginx
• Security — API keys, CORS, input validation

Nima uchun aynan bu 4 ta?

Loyiha 1 — Klassik ML (oson, lekin to'liq)

• **Maqsad:**End-to-end ML lifecycle'ni ko'rsatish
• **Highlight:**Reproducibility, monitoring
• Vakansiyalar:"Junior ML Engineer", "Data Scientist"

Loyiha 2 — Computer Vision (Deep Learning)

• **Maqsad:**DL'ni production'da ishlata olishni ko'rsatish
• **Highlight:**GPU optimization, async processing
• Vakansiyalar:"Computer Vision Engineer", "ML Engineer"

Loyiha 3 — RAG/LLM (Modern AI)

• **Maqsad:**AI Product engineering ko'nikmasi
• **Highlight:**LLM expertise, vector DB, system design
• Vakansiyalar:"AI Engineer", "LLM Engineer", "GenAI Engineer"

Loyiha 4 — MLOps Platform (eng murakkab)

• **Maqsad:**Sizning asosiy maqsadingiz — MLOps Engineer
• **Highlight:**Sistema arxitekturasi, multi-tool integration
• Vakansiyalar:"MLOps Engineer", "ML Platform Engineer", "Senior ML Engineer"

Standart loyiha strukturasi

project-name/
├── README.md                       # Asosiy
├── ARCHITECTURE.md                 # System design
├── docker-compose.yml
├── Dockerfile
├── .github/
│   └── workflows/
│       ├── ci.yml
│       └── deploy.yml
├── src/
│   ├── api/                        # FastAPI endpoints
│   ├── core/                       # Business logic
│   ├── data/                       # Data layer
│   ├── ml/                         # ML/model code
│   └── utils/
├── tests/
│   ├── unit/
│   ├── integration/
│   └── e2e/
├── notebooks/                      # Exploration
├── data/                           # DVC tracked
├── models/                         # MLflow tracked
├── k8s/ (yoki helm/)               # Deployment manifests
├── monitoring/                     # Prometheus, Grafana configs
├── docs/                           # Additional docs
├── scripts/                        # Utility scripts
├── pyproject.toml
├── requirements.txt
├── requirements-dev.txt
├── .env.example
├── .gitignore
├── .dockerignore
└── Makefile                        # Common commands

Loyiha boshlash checklist

Yangi loyihani boshlashdan oldin:

• GitHub repo yarating (public)
• Initial README (loyihaning maqsadi)
• Architecture diagram
• Tech stack tanlash (sabablar bilan)
• User stories yoki use cases
• MVP definition (1 hafta uchun)
• Roadmap (haftalik milestones)

Portfolio prezentatsiyasi

Loyiha tugagandan keyin:

1. LinkedIn post(template):

🚀 Yangi loyiha: [LOYIHA NOMI]

Vazifa: [bir gap]

Tech stack:
🔹 [tech 1]
🔹 [tech 2]
🔹 [tech 3]

Key achievements:
✅ [natija 1]
✅ [natija 2]
✅ [natija 3]

GitHub: [link]
Demo: [link]
Blog: [link]

#MLOps #MachineLearning #Python

cc: @jahongir-hakimjonov — "Backend to ML Roadmap" muallifi
(loyihangizni LinkedIn'da ulashganda muallifni tag qiling — yordam yoki review kerak bo'lsa, javob beraman)

2. CV'ga qo'shish:

Project: [LOYIHA NOMI] (date)
- Tech: Python, FastAPI, Docker, K8s, MLflow, ...
- Built end-to-end ML system: [bir gap haqida]
- Achieved [aniq metric]
- GitHub: [link]

3. Portfolio website: yourname.dev

• 4 ta loyihaning galereyasi
• Har biri uchun: image, description, links

Interview preparation

Har bir loyiha haqida shu savollarga javob tayyorlang:

• Why this project?(motivatsiya)
• What's the architecture?(tushuntirish + diagram)
• What were the challenges?(texnik)
• What would you do differently?(refleksiya)
• How would you scale it 10x?(sistema dizayni)
• What metrics define success?(mahsulot tushunchasi)
• Show me the code(jonli)

Mukammal natija uchun maslahatlar

1. Sifat > Miqdor — 4 ta zo'r loyiha 10 ta o'rtachadan yaxshiroq
2. Real-world data — toy datasets'dan tashqari
3. Documentation — coddan ham muhim
4. Demo video — recruiter'lar README o'qimaydi, lekin video ko'radi
5. Open source — pull request'lar qabul qiling
6. Blogging — har loyihaga texnik post yozing
7. GitHub README — emoji, badges, diagrams, screenshots

Boshlash

Loyiha 1: Prediction API bilan boshlang.