Final Loyihalar (Portfolio)

🎯 Goal

4 large projectsthat put into practice what you’ve learned over 6 months. These are your:

• GitHub portfoliongiz
• CV’dagi “Projects”
• Your material for interviews
• LinkedIn postlaringiz

4 projects

Requirements for each project (minimum)

Texnik

• GitHub in public repo(clear README)
• Docker + docker-compose — runs with a single command
• Tests — pytest, kamida 50% coverage
• CI/CD — GitHub Actions
• API documentation — OpenAPI/Swagger
• Architecture diagram(Mermaid or Excalidraw)
• Environment variables — .env.example faylda

Code Quality

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

Documentation

• README — installation, usage, API examples
• Architecture explanation — qaror reasons
• Demo video — Loom (5-10 daqiqa)
• Blog post — Medium/dev.to (for each one)

Production

• Healthcheck endpoint — /health
• Logging — structured (JSON)
• Error handling — Sentry or similar
• Rate limiting — slowapi or nginx
• Security — API keys, CORS, input validation

Why these 4 specifically?

Project 1 — Classical ML (easy but complete)

• **Goal:**Demonstrate end-to-end ML lifecycle
• **Highlight:**Reproducibility, monitoring
• Vakansiyalar:“Junior ML Engineer”, “Data Scientist”

Project 2 — Computer Vision (Deep Learning)

• **Goal:**To demonstrate the ability to use DL in production
• **Highlight:**GPU optimization, async processing
• Vakansiyalar:“Computer Vision Engineer”, “ML Engineer”

Project 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”

Project 4 — MLOps Platform (most complex)

• **Goal:**Your main goal — 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

Before starting a new project:

• GitHub repo yarating (public)
• Initial README (loyihaning maqsadi)
• Architecture diagram
• Tech stack selection (with reasons)
• User stories or use cases
• MVP definition (for 1 week)
• Roadmap (haftalik milestones)

Portfolio prezentatsiyasi

Loyiha tugagandan keyin:

1. LinkedIn post(template):

🚀 New project: [PROJECT NAME]

Task: [one sentence]

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

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

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

#MLOps #MachineLearning #Python

cc: @jahongir-hakimjonov — author of "Backend to ML Roadmap"
(when sharing your project on LinkedIn, tag the author — happy to help or review)

2. CV to 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
• For each: image, description, links

Interview preparation

Prepare answers to these questions about each project:

• 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)

Tips for a perfect result

1. Sifat > Miqdor — 4 ta great loyiha 10 ta o’rtachadan yaxshiroq
2. Real-world data — beyond toy datasets
3. Documentation — coddan ham muhim
4. Demo video — recruiters README o’qimaydi, lekin video ko’radi
5. Open source — accept pull requests
6. Blogging — har loyihaga texnik post yozing
7. GitHub README — emoji, badges, diagrams, screenshots

Start

Start with Project 1: Prediction API.