EzeniIa

Deployment Guide

Learn to deploy smart contracts, AI models, and web applications with optimized infrastructure and security practices.

Start Deploying →

Deployment Guide Outline

1. Deployment Overview

Deployment is the final stage where your blockchain contracts, machine learning models, and web applications become live on production networks. This guide covers best practices for secure and efficient deployment across different environments.

"Choose your deployment network as carefully as you choose your code base." - Solidity Best Practices

Why Proper Deployment Matters

  • Ensures contract immutability and security on blockchain
  • Optimizes AI model performance and cost efficiency
  • Guarantees uptime and reliability for web applications
2.

Deployment Environments

Test Networks

  • • Rinkeby/Ethereum-Goerli (Blockchain)
  • • localhost:8545 (Hardhat)
  • • Stage deployment servers
  • • CI/CD pipelines

Production Environments

  • • Ethereum mainnet
  • • Binance Smart Chain
  • • Live web servers
  • • Containerized deployments (Docker)
3.

Smart Contract Deployment

{`// Hardhat deployment script async function main() { const contractFactory = await hre.ethers.getContractFactory("YourContract"); const contract = await contractFactory.deploy(); await contract.deployTransaction.wait(); console.log(`Deployed to: ${contract.address}`); }`}
🔧

Local Testing

Test with npx hardhat node before mainnet deployment

Gas Optimization

Use --network mainnet with custom gas limits

4.

AI Model Deployment

Python Model

{`# Flask model API deployment from flask import Flask, request import joblib app = Flask(__name__) model = joblib.load("trained_model.pkl") @app.route("/predict", methods=["POST"]) def predict(): data = request.get_json() prediction = model.predict([data]) return {"result": prediction.tolist()} `}
✅ GPU support
⚙️ Auto-scaling

TensorFlow Model

{`# TensorFlow serving command docker run -it --rm -p 8501:8080 \\ -v "$(pwd)":/models/ \\ -e MODEL_NAME="my_model" \\ tensorflow/serving`) }
📦 Model versioning
🚀 REST/gRPC support
5.

Web Application Deployment

🚀

Vercel/Next.js

All-in-one deploy for React/Next.js apps

📦

Docker + Kubernetes

Containerized deployments with orchestration

🧠

Cloud Solutions

AWS, Google Cloud, Azure deployment options

Pro Tip

Use CI/CD pipelines for automated deployments when working with blockchain and machine learning projects.

6.

Deployment Security

7.

Post-Deployment Monitoring

Blockchain Analytics

  • • Etherscan/Blockchair monitoring
  • • Event logging and error tracking
  • • Gas usage analytics
  • • Transaction confirmation checks

Health Monitoring

  • • Uptime monitoring
  • • Error budget tracking
  • • Performance alerts
  • • Cost monitoring

Security Monitoring

  • • Vulnerability scanners
  • • Access log monitoring
  • • Smart contract watchdog
  • • Attack pattern detecting

User Activity

  • • Transaction volume tracking
  • • Active users analytics
  • • Token usage patterns
  • • Referral system monitoring