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if using custom domain)\n- GitHub Actions (CI/CD)\n\n**Machine Learning (Phase 2):**\n\n- Python 3.11\n- scikit-learn\n- AWS SageMaker\n- Docker (model containerization)\n\n## Features\n\n- ✅ Serverless architecture (auto-scaling, cost-optimized)\n- ✅ Global CDN with CloudFront (\u003c 100ms load times)\n- ✅ Automated CI/CD pipeline\n- ✅ Contact form with email notifications\n- ✅ Responsive design (mobile-first)\n- ✅ SEO optimized\n- 🚧 ML-powered service recommendation engine (Phase 2)\n- 🚧 Infrastructure as Code with Terraform (Phase 3)\n\n## Deployment\n\n**S3 Static Website URL:**\n\nhttps://evanmarshall-tech-services-2026.s3-website-us-east-1.amazonaws.com\n\nAutomated deployment via GitHub Actions:\n\n1. Push to `main` branch\n2. GitHub Actions builds Next.js app\n3. Syncs to S3 bucket\n4. Invalidates CloudFront cache\n5. Site live in ~2 minutes\n\n**Manual deployment:**\n\n```bash\nnpm run build\naws s3 sync out/ s3://your-bucket-name/ --delete\naws cloudfront create-invalidation --distribution-id YOUR_ID --paths \"/*\"\n```\n\n## What I Learned\n\nThis project taught me:\n\n- Designing serverless architectures on AWS\n- Implementing CI/CD pipelines with GitHub Actions\n- Building production-ready React applications\n- Cost optimization in cloud infrastructure (~$10/month for production app)\n- API design and Lambda function development\n- (Phase 2) Deploying ML models to production\n- (Phase 3) Managing infrastructure with Terraform\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevanmarshall-dev%2Fevanmarshall-tech","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fevanmarshall-dev%2Fevanmarshall-tech","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevanmarshall-dev%2Fevanmarshall-tech/lists"}