Documentation / deployment/ R E A D M E

🚀 Deployment Guide

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Table of Contents

Prerequisites

System Requirements

Component Requirement
Node.js v16.x or higher
Python v3.8 or higher
Docker v20.x or higher
Hardhat v2.9.x or higher
Solidity ^0.8.0

Environment Setup

# Install dependencies
npm install

# Install Python requirements
pip install -r requirements.txt

# Configure environment
cp .env.example .env

Smart Contract Deployment

Deployment Flow

sequenceDiagram
    participant Dev
    participant Local
    participant Test
    participant Prod

    Dev->>Local: Local Testing
    Local->>Test: Testnet Deploy
    Test->>Dev: Validation
    Dev->>Prod: Production Deploy
    Prod->>Dev: Verification

Contract Deployment Steps

# 1. Compile contracts
npx hardhat compile

# 2. Run tests
npx hardhat test

# 3. Deploy to testnet
npx hardhat deploy --network goerli

# 4. Verify contracts
npx hardhat verify --network goerli <CONTRACT_ADDRESS>

Configuration Example

module.exports = {
    networks: {
        hardhat: {
            chainId: 1337
        },
        goerli: {
            url: process.env.GOERLI_URL,
            accounts: [process.env.PRIVATE_KEY]
        },
        mainnet: {
            url: process.env.MAINNET_URL,
            accounts: [process.env.PRIVATE_KEY]
        }
    },
    solidity: {
        version: "0.8.17",
        settings: {
            optimizer: {
                enabled: true,
                runs: 200
            }
        }
    }
};

ML Service Deployment

Architecture Overview

graph TD
    subgraph ML Infrastructure
        A[Data Pipeline]
        B[Training Service]
        C[Inference Service]
        D[Model Registry]
    end
    
    subgraph Deployment
        E[Kubernetes Cluster]
        F[Load Balancer]
        G[Monitoring]
    end
    
    A --> B
    B --> D
    D --> C
    C --> E
    E --> F
    E --> G

The ML service architecture consists of several key components:

  1. Data Pipeline: Collects and processes data for model training and inference
  2. Training Service: Handles model training and validation
  3. Inference Service: Serves model predictions in production
  4. Model Registry: Stores and version controls trained models
  5. Deployment Infrastructure: Manages the production environment

Docker Configuration

# Base image
FROM pytorch/pytorch:latest

# Install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy application code
COPY . /app
WORKDIR /app

# Set environment variables
ENV MODEL_PATH=/app/models
ENV CONFIG_PATH=/app/config

# Expose port
EXPOSE 8080

# Run application
CMD ["python", "app/main.py"]

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: riskify-ml
spec:
  replicas: 3
  selector:
    matchLabels:
      app: riskify-ml
  template:
    metadata:
      labels:
        app: riskify-ml
    spec:
      containers:
      - name: riskify-ml
        image: riskify/ml-service:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            cpu: "2"
            memory: "4Gi"
          requests:
            cpu: "1"
            memory: "2Gi"

Infrastructure Setup

Network Architecture

graph TB
    subgraph External
        CLI["Clients"]
        API["API Gateway"]
    end

    subgraph Services
        WEB["Web Server"]
        APP["Application"]
        ML["ML Service"]
    end

    subgraph Data
        DB["Database"]
        CACHE["Cache"]
        MQ["Message Queue"]
    end

    External --> Services
    Services --> Data

Load Balancing

graph LR
    subgraph Load Balancer
        LB["NGINX"]
        HC["Health Check"]
    end

    subgraph Backend
        S1["Server 1"]
        S2["Server 2"]
        S3["Server 3"]
    end

    LB --> S1
    LB --> S2
    LB --> S3
    HC --> S1
    HC --> S2
    HC --> S3

Monitoring & Maintenance

Monitoring Architecture

graph TB
    %% Styling
    classDef collection fill:#4a5568,stroke:#2d3748,color:white,stroke-width:2px,font-weight:bold
    classDef processing fill:#718096,stroke:#4a5568,color:white,stroke-width:2px,font-weight:bold
    classDef visualization fill:#718096,stroke:#4a5568,color:white,stroke-width:2px,font-weight:bold
    classDef node fill:#f7fafc,stroke:#e2e8f0,color:#2d3748,stroke-width:1px

    subgraph DataCollection["Data Collection"]
        direction TB
        LOG["Logs"]
        MET["Metrics"]
        TRC["Traces"]
    end

    subgraph Processing["Processing"]
        direction TB
        AGG["Aggregation"]
        ANA["Analysis"]
        ALT["Alerting"]
    end

    subgraph Visualization["Visualization"]
        direction TB
        DASH["Dashboards"]
        REP["Reports"]
        ALERTS["Alerts"]
    end

    DataCollection --> Processing
    Processing --> Visualization

    %% Apply styles
    class DataCollection collection
    class Processing processing
    class Visualization visualization
    class LOG,MET,TRC,AGG,ANA,ALT,DASH,REP,ALERTS node

Key Metrics

Category Metric Threshold
Performance Response Time < 100ms
Reliability Uptime > 99.9%
Resource CPU Usage < 80%
Security Failed Auth < 1%
ML Model Accuracy > 95%

Security Considerations

Security Architecture

graph TB
    subgraph Perimeter
        FW["Firewall"]
        WAF["Web Application Firewall"]
        DDoS["DDoS Protection"]
    end

    subgraph Authentication
        IAM["Identity Management"]
        MFA["Multi-Factor Auth"]
        JWT["JWT Tokens"]
    end

    subgraph Monitoring
        SIEM["Security Monitoring"]
        IDS["Intrusion Detection"]
        AUD["Audit Logs"]
    end

    Perimeter --> Authentication
    Authentication --> Monitoring

Security Checklist

Category Item Status
Access Control Role-based Access Required
Authentication Multi-factor Auth Required
Network TLS 1.3 Required
Monitoring Audit Logging Required
Compliance GDPR Required

Next Steps

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