Projects

Hands-on Projects

Practical projects to reinforce your ML engineering skills.

Beginner Projects

1. Anomaly Detection System

Objective: Build a system to detect anomalies in server metrics

Skills Practiced:

• Time series analysis
• Unsupervised learning
• Real-time processing
• Alert generation

Tech Stack: Python, Scikit-learn, Prometheus, Grafana

2. Log Classification Pipeline

Objective: Automatically categorize and route system logs

Skills Practiced:

• Text processing
• Classification algorithms
• Stream processing
• Pipeline orchestration

Tech Stack: Python, Kafka, Elasticsearch, Airflow

Intermediate Projects

3. Predictive Auto-scaling

Objective: ML-based auto-scaling for Kubernetes workloads

Skills Practiced:

• Time series forecasting
• Infrastructure automation
• Model deployment
• Performance optimization

Tech Stack: Python, Kubernetes, Prometheus, ARIMA/LSTM

4. CI/CD Pipeline for ML

Objective: Complete MLOps pipeline with automated testing and deployment

Skills Practiced:

• Version control (Git, DVC)
• Automated testing
• Model validation
• Progressive deployment

Tech Stack: GitHub Actions, MLflow, Docker, Kubernetes

Advanced Projects

5. Multi-Model Serving Platform

Objective: Build a platform to serve multiple ML models with A/B testing

Skills Practiced:

• Model registry
• Load balancing
• A/B testing
• Performance monitoring

Tech Stack: FastAPI, Redis, Kubernetes, Prometheus

6. Federated Learning System

Objective: Implement privacy-preserving distributed model training

Skills Practiced:

• Distributed systems
• Privacy techniques
• Model aggregation
• Security practices

Tech Stack: PyTorch, gRPC, Docker, Kubernetes

Capstone Project

End-to-End ML Platform

Build a complete ML platform that includes:

Phase 1: Data Pipeline

• Ingest data from multiple sources
• Implement data validation
• Create feature store
• Set up data versioning

Phase 2: Training Pipeline

• Automated model training
• Hyperparameter tuning
• Experiment tracking
• Model registry

Phase 3: Deployment & Serving

• Containerized deployment
• Auto-scaling based on load
• Model versioning
• Rollback capabilities

Phase 4: Monitoring & Maintenance

• Performance monitoring
• Data drift detection
• Automated retraining
• Alert system

Deliverables

1. Source code repository
2. Documentation
3. Architecture diagrams
4. Performance benchmarks
5. Cost analysis

Evaluation Criteria

• Code quality and organization
• System reliability
• Performance optimization
• Documentation completeness
• Security considerations

Project Templates

Basic ML Service Template

project-template/
├── api/
│   ├── __init__.py
│   ├── main.py
│   └── models.py
├── ml/
│   ├── __init__.py
│   ├── preprocessing.py
│   ├── training.py
│   └── inference.py
├── tests/
│   ├── test_api.py
│   └── test_ml.py
├── docker/
│   └── Dockerfile
├── k8s/
│   ├── deployment.yaml
│   └── service.yaml
├── .github/
│   └── workflows/
│       └── ci.yml
├── requirements.txt
└── README.md

Submission Guidelines

Code Requirements

• Clean, documented code
• Unit tests with >80% coverage
• Integration tests
• Performance benchmarks

Documentation

• README with setup instructions
• Architecture documentation
• API documentation
• Deployment guide

Presentation

• Problem statement
• Solution approach
• Technical challenges
• Results and metrics
• Future improvements

Resources for Projects

Datasets

• Kaggle Competitions
• Google Cloud Public Datasets
• AWS Open Data

Compute Resources

• Google Colab (Free GPU)
• Kaggle Kernels (Free GPU)
• AWS Free Tier
• Azure Free Account

Example Implementations

• MLOps Examples
• Kubeflow Examples
• TFX Examples