# technical-readiness-assessment.yaml
assessment_categories:
  infrastructure_modernization:
    current_state_evaluation:
      - "Infrastructure as Code adoption level"
      - "Cloud vs. on-premises distribution"
      - "Legacy system dependencies"
      - "Monitoring and logging maturity"
      - "Security integration status"
    
    scoring_criteria:
      level_1_basic: "Manual infrastructure, limited automation"
      level_2_emerging: "Some automation, partial IaC adoption"
      level_3_advanced: "Comprehensive automation, full IaC"
      level_4_optimized: "Self-service platforms, advanced automation"
    
    current_score: "level_2_emerging"
    improvement_priority: "High"
    recommended_actions:
      - "Implement basic IaC practices"
      - "Establish monitoring foundations"
      - "Begin infrastructure standardization"

  application_architecture:
    current_state_evaluation:
      - "Monolith vs. microservices distribution"
      - "API maturity and documentation"
      - "Database modernization status"
      - "Integration patterns and standards"
      - "Technology stack diversity"
    
    scoring_criteria:
      level_1_basic: "Predominantly monolithic, tight coupling"
      level_2_emerging: "Some service decomposition, basic APIs"
      level_3_advanced: "Microservices, well-defined APIs"
      level_4_optimized: "Cloud-native, event-driven architecture"
    
    current_score: "level_2_emerging"
    improvement_priority: "Medium"
    recommended_actions:
      - "Identify candidates for service decomposition"
      - "Establish API standards and documentation practices"
      - "Plan modernization for legacy components"

  deployment_practices:
    current_state_evaluation:
      - "Deployment frequency and automation"
      - "Release process complexity"
      - "Rollback and recovery capabilities"
      - "Testing automation level"
      - "Environment parity"
    
    scoring_criteria:
      level_1_basic: "Manual deployments, infrequent releases"
      level_2_emerging: "Some automation, weekly releases"
      level_3_advanced: "Fully automated, daily releases"
      level_4_optimized: "Continuous deployment, real-time releases"
    
    current_score: "level_1_basic"
    improvement_priority: "High"
    recommended_actions:
      - "Implement basic CI/CD pipelines"
      - "Establish automated testing practices"
      - "Create standardized environments"

  monitoring_and_observability:
    current_state_evaluation:
      - "System monitoring coverage"
      - "Application performance monitoring"
      - "Log aggregation and analysis"
      - "Alerting and incident response"
      - "Business metrics tracking"
    
    scoring_criteria:
      level_1_basic: "Basic system monitoring only"
      level_2_emerging: "Application monitoring, basic alerting"
      level_3_advanced: "Comprehensive monitoring, automated alerts"
      level_4_optimized: "Full observability, predictive analytics"
    
    current_score: "level_2_emerging"
    improvement_priority: "Medium"
    recommended_actions:
      - "Implement distributed tracing"
      - "Establish business metrics monitoring"
      - "Create comprehensive alerting strategies"

# cultural-readiness-assessment.yaml
cultural_dimensions:
  collaboration_readiness:
    evaluation_factors:
      - "Cross-team communication frequency"
      - "Shared responsibility practices"
      - "Knowledge sharing behaviors"
      - "Blame vs. learning culture orientation"
      - "Change acceptance patterns"
    
    measurement_approach:
      surveys: "Quarterly cultural assessment surveys"
      interviews: "One-on-one interviews with key stakeholders"
      observation: "Behavioral observation during incidents"
      metrics_analysis: "Analysis of collaboration metrics"
    
    current_state: "Low collaboration, high silo mentality"
    improvement_target: "High collaboration, shared responsibility"
    gap_analysis:
      - "Limited cross-functional team structures"
      - "Siloed performance metrics"
      - "Lack of shared success metrics"
      - "Inadequate communication channels"

  leadership_support:
    evaluation_factors:
      - "Executive commitment to DevOps principles"
      - "Resource allocation for transformation"
      - "Risk tolerance for experimentation"
      - "Change management capabilities"
      - "Communication about transformation"
    
    measurement_approach:
      - "Leadership interview assessment"
      - "Budget allocation analysis"
      - "Decision-making process evaluation"
      - "Communication frequency and quality"
    
    current_state: "Moderate support, limited resource commitment"
    improvement_target: "Strong support, adequate resource allocation"
    gap_analysis:
      - "Insufficient budget allocation"
      - "Limited executive understanding of DevOps"
      - "Risk-averse decision making culture"

  skill_and_capability:
    evaluation_factors:
      - "Technical skills for DevOps practices"
      - "Process and methodology knowledge"
      - "Change management capabilities"
      - "Training and development investment"
      - "Learning agility and adaptability"
    
    measurement_approach:
      - "Skills assessment surveys"
      - "Certification tracking"
      - "Performance evaluation analysis"
      - "Training program effectiveness"
    
    current_state: "Basic skills, limited DevOps experience"
    improvement_target: "Advanced skills, DevOps expertise"
    gap_analysis:
      - "Limited automation skills"
      - "Insufficient cloud platform knowledge"
      - "Lack of security integration skills"
      - "Inadequate monitoring and observability skills"

# Executive Sponsorship Strategy

## Primary Sponsor Identification
- **Role**: CTO/VP of Engineering
- **Responsibilities**:
  - Champion DevOps transformation
  - Allocate necessary resources
  - Remove organizational obstacles
  - Communicate vision and progress

## Secondary Sponsors
- **Business Unit Leaders**: Ensure alignment with business objectives
- **Operations Directors**: Support operational aspects of transformation
- **Security Leadership**: Ensure security integration and compliance
- **Finance Leadership**: Support budget allocation and cost justification

## Sponsor Engagement Activities
- Monthly steering committee meetings
- Quarterly business impact reviews
- Annual transformation assessments
- Regular communication to broader organization

# DevOps Business Case

## Financial Justification

### Cost Reduction Opportunities
- **Manual Process Automation**: $2M annually
  - Reduce manual deployment time by 80%
  - Eliminate manual testing overhead
  - Reduce incident response time

- **Infrastructure Optimization**: $1.5M annually
  - Right-size cloud resources
  - Implement auto-scaling
  - Optimize licensing costs

- **Quality Improvements**: $3M annually
  - Reduce production incidents by 60%
  - Decrease customer-impacting bugs by 50%
  - Lower support and maintenance costs

### Revenue Enhancement Opportunities
- **Faster Time to Market**: $5M annually
  - Increase deployment frequency by 10x
  - Reduce feature delivery time by 50%
  - Enable faster response to market changes

- **Improved Customer Experience**: $4M annually
  - Higher application availability (99.9% to 99.99%)
  - Better performance and responsiveness
  - Enhanced reliability and trust

## Total Economic Impact
- **Initial Investment**: $2.5M (first year)
- **Annual Benefits**: $10.5M (year two and beyond)
- **ROI Timeline**: 6 months to breakeven
- **3-Year NPV**: $24M

# cicd-platform-setup.yaml
platform_requirements:
  version_control:
    tool: "GitLab Enterprise Edition"
    features_needed:
      - "Protected branches and merge request approvals"
      - "Code review workflows"
      - "Integration with CI/CD"
      - "Access control and permissions"
    
    implementation_timeline:
      month_1: "GitLab installation and configuration"
      month_2: "Migration of existing repositories"
      month_3: "Branch protection and approval workflows"
  
  ci_cd_automation:
    tool: "GitLab CI/CD with custom templates"
    pipeline_features:
      - "Multi-stage pipelines"
      - "Parallel job execution"
      - "Integration with testing frameworks"
      - "Security scanning integration"
    
    implementation_stages:
      stage_1: "Basic build and test pipelines"
      stage_2: "Security scanning integration"
      stage_3: "Multi-environment deployments"
      stage_4: "Advanced deployment strategies"

  artifact_management:
    tool: "GitLab Container Registry"
    requirements:
      - "Docker image storage and management"
      - "Security scanning of images"
      - "Image retention policies"
      - "Access control and permissions"
    
    implementation_steps:
      step_1: "Registry setup and configuration"
      step_2: "Integration with CI/CD pipelines"
      step_3: "Security scanning implementation"
      step_4: "Retention policy enforcement"

  monitoring_and_observability:
    tools: ["Prometheus", "Grafana", "ELK Stack"]
    implementation_phases:
      phase_1: "Basic system monitoring setup"
      phase_2: "Application performance monitoring"
      phase_3: "Log aggregation and analysis"
      phase_4: "Alerting and dashboard creation"

# terraform-foundation/main.tf
terraform {
  required_version = ">= 1.0"
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }

  backend "s3" {
    bucket         = "company-terraform-state"
    key            = "terraform.tfstate"
    region         = "us-east-1"
    encrypt        = true
    dynamodb_table = "terraform-state-lock"
  }
}

provider "aws" {
  region = var.aws_region
}

# Foundation resources
resource "aws_s3_bucket" "terraform_state" {
  bucket = "company-terraform-state"

  versioning {
    enabled = true
  }

  server_side_encryption_configuration {
    rule {
      apply_server_side_encryption_by_default {
        s3_kms_key_id = aws_kms_key.state_encryption.arn
      }
    }
  }

  tags = {
    Name        = "Terraform State Bucket"
    Environment = "foundation"
  }
}

resource "aws_dynamodb_table" "terraform_locks" {
  name         = "terraform-state-lock"
  billing_mode = "PAY_PER_REQUEST"

  hash_key = "LockID"

  attribute {
    name = "LockID"
    type = "S"
  }

  tags = {
    Name        = "Terraform State Lock Table"
    Environment = "foundation"
  }
}

resource "aws_kms_key" "state_encryption" {
  description = "KMS key for Terraform state encryption"

  tags = {
    Name        = "Terraform State Encryption Key"
    Environment = "foundation"
  }
}

resource "aws_kms_alias" "state_encryption" {
  name          = "alias/terraform-state-encryption"
  target_key_id = aws_kms_key.state_encryption.key_id
}

# Shared VPC foundation
module "vpc" {
  source = "./modules/vpc"

  name               = "company-shared-vpc"
  cidr_block         = "10.0.0.0/16"
  azs                = var.availability_zones
  public_subnets     = var.public_subnets
  private_subnets    = var.private_subnets
  database_subnets   = var.database_subnets
  create_nat_gateway = true
  enable_dns_hostnames = true
  enable_dns_support   = true

  tags = {
    Name        = "Company Shared VPC"
    Environment = "shared"
  }
}

# Outputs
output "vpc_id" {
  description = "ID of the created VPC"
  value       = module.vpc.vpc_id
}

output "public_subnet_ids" {
  description = "IDs of the public subnets"
  value       = module.vpc.public_subnet_ids
}

output "private_subnet_ids" {
  description = "IDs of the private subnets"
  value       = module.vpc.private_subnet_ids
}

# Variables
variable "aws_region" {
  description = "AWS region for resources"
  type        = string
  default     = "us-east-1"
}

variable "availability_zones" {
  description = "List of availability zones"
  type        = list(string)
  default     = ["us-east-1a", "us-east-1b", "us-east-1c"]
}

variable "public_subnets" {
  description = "List of public subnet CIDR blocks"
  type        = list(string)
  default     = ["10.0.1.0/24", "10.0.2.0/24", "10.0.3.0/24"]
}

variable "private_subnets" {
  description = "List of private subnet CIDR blocks"
  type        = list(string)
  default     = ["10.0.101.0/24", "10.0.102.0/24", "10.0.103.0/24"]
}

variable "database_subnets" {
  description = "List of database subnet CIDR blocks"
  type        = list(string)
  default     = ["10.0.201.0/24", "10.0.202.0/24", "10.0.203.0/24"]
}

# team-structure.yaml
devops_team_structure:
  team_formation_phase:
    month_1_2:
      - "Hire or assign DevOps architect"
      - "Recruit platform engineers (2-3)"
      - "Establish team reporting structure"
      - "Define team mission and objectives"
    
    month_3_4:
      - "Hire or assign security engineer"
      - "Recruit automation engineers (2-3)"
      - "Establish team processes and practices"
      - "Begin cross-training programs"
    
    month_5_6:
      - "Hire or assign reliability engineer"
      - "Complete team staffing"
      - "Establish team metrics and KPIs"
      - "Begin pilot project implementation"

  role_definitions:
    devops_architect:
      responsibilities:
        - "Technical architecture and design"
        - "Tool selection and integration"
        - "Standards and best practices definition"
        - "Cross-team technical guidance"
      qualifications:
        - "10+ years of experience"
        - "Cloud platform expertise"
        - "Infrastructure as Code mastery"
        - "Security integration knowledge"
      reporting_structure: "Reports to VP of Engineering"
    
    platform_engineer:
      responsibilities:
        - "Internal developer platform development"
        - "Tool chain integration and maintenance"
        - "Infrastructure automation"
        - "Developer experience optimization"
      qualifications:
        - "5+ years of experience"
        - "Programming skills (Python, Go, or Java)"
        - "Infrastructure as Code experience"
        - "Container and orchestration knowledge"
      reporting_structure: "Reports to DevOps Architect"
    
    automation_engineer:
      responsibilities:
        - "CI/CD pipeline development and maintenance"
        - "Testing automation"
        - "Deployment automation"
        - "Process optimization"
      qualifications:
        - "3+ years of experience"
        - "CI/CD platform expertise"
        - "Scripting and automation skills"
        - "Testing framework knowledge"
      reporting_structure: "Reports to DevOps Architect"

  team_governance:
    decision_making:
      - "Technical decisions: Architect approval required"
      - "Process decisions: Team consensus"
      - "Resource allocation: Management approval"
      - "Tool selection: Architecture council review"
    
    communication_protocols:
      - "Daily standups: 15 minutes, 9 AM"
      - "Weekly planning: 1 hour, Monday morning"
      - "Monthly retrospectives: 2 hours, last Friday"
      - "Quarterly planning: Half day, first week of quarter"
    
    escalation_processes:
      - "Technical issues: Architect first, then management"
      - "Process issues: Team discussion, then management"
      - "Resource issues: Direct to management"
      - "Stakeholder issues: Team lead to sponsor"

# training-program.yaml
training_curriculum:
  foundation_module:
    duration: "2 weeks"
    topics:
      - "DevOps principles and culture"
      - "Agile and Lean methodologies"
      - "Version control best practices"
      - "Basic automation concepts"
    delivery_method: "Instructor-led workshop"
    participants: "All team members"
    prerequisites: "None"
    success_criteria:
      - "Pass knowledge assessment with 80%+"
      - "Complete hands-on labs"
      - "Demonstrate basic concepts"

  technical_skills_module:
    duration: "4 weeks"
    topics:
      - "Infrastructure as Code (Terraform)"
      - "CI/CD pipeline development"
      - "Containerization and orchestration"
      - "Monitoring and observability"
    delivery_method: "Blended: online courses + hands-on labs"
    participants: "Platform and automation engineers"
    prerequisites: "Foundation module completion"
    success_criteria:
      - "Complete certification exam"
      - "Build working demo environment"
      - "Pass practical assessment"

  security_integration_module:
    duration: "2 weeks"
    topics:
      - "DevSecOps principles"
      - "Security scanning integration"
      - "Compliance automation"
      - "Secrets management"
    delivery_method: "Workshop with security team"
    participants: "All DevOps team members"
    prerequisites: "Technical skills module completion"
    success_criteria:
      - "Pass security awareness training"
      - "Implement security scanning in pipeline"
      - "Configure secrets management"

  advanced_practices_module:
    duration: "3 weeks"
    topics:
      - "Advanced monitoring and observability"
      - "Performance optimization"
      - "Chaos engineering"
      - "Advanced deployment strategies"
    delivery_method: "Mentored learning with experts"
    participants: "Senior team members"
    prerequisites: "Previous modules completion"
    success_criteria:
      - "Lead advanced implementation project"
      - "Mentor junior team members"
      - "Present findings to leadership"

training_schedule:
  month_1:
    - "Foundation module (Week 1-2)"
    - "Begin technical skills module (Week 3-4)"
  
  month_2:
    - "Continue technical skills module"
    - "Complete security integration module"
  
  month_3:
    - "Begin advanced practices module"
    - "Start pilot project implementation"
  
  month_4:
    - "Complete advanced practices module"
    - "Continue pilot project development"
  
  month_5:
    - "Advanced project implementation"
    - "Mentoring and knowledge sharing"
  
  month_6:
    - "Training program evaluation"
    - "Continuous learning plan development"

# pilot-project-selection.yaml
pilot_selection_criteria:
  technical_feasibility:
    - "Application is well-understood"
    - "Limited external dependencies"
    - "Non-critical business impact"
    - "Willing development team"
    - "Clear success metrics"
  
  business_value:
    - "Demonstrates DevOps benefits"
    - "Visible to stakeholders"
    - "Measurable impact"
    - "Potential for expansion"
    - "Good learning opportunity"
  
  risk_factors:
    - "Low customer impact if unsuccessful"
    - "Limited data sensitivity"
    - "No regulatory compliance concerns"
    - "Manageable complexity"
    - "Adequate team support"

selected_pilot_projects:
  project_1:
    name: "Customer portal API"
    business_unit: "Customer Experience"
    development_team: "Customer Experience Team"
    application_type: "REST API service"
    current_state:
      deployment_frequency: "Monthly"
      lead_time: "2-3 weeks"
      failure_rate: "5%"
      mttr: "2 hours"
    success_metrics:
      deployment_frequency: "Daily"
      lead_time: "< 1 day"
      failure_rate: "< 2%"
      mttr: "< 30 minutes"
    timeline:
      setup: "Month 7"
      pipeline_development: "Month 8"
      testing_and_validation: "Month 9"
      production_rollout: "Month 10"
      stabilization: "Month 11-12"

  project_2:
    name: "Internal reporting dashboard"
    business_unit: "Business Intelligence"
    development_team: "BI Team"
    application_type: "Web application"
    current_state:
      deployment_frequency: "Quarterly"
      lead_time: "4-6 weeks"
      failure_rate: "8%"
      mttr: "4 hours"
    success_metrics:
      deployment_frequency: "Weekly"
      lead_time: "< 3 days"
      failure_rate: "< 3%"
      mttr: "< 1 hour"
    timeline:
      setup: "Month 8"
      pipeline_development: "Month 9-10"
      testing_and_validation: "Month 11"
      production_rollout: "Month 12"

# pilot-implementation-pipeline.yaml
customer_portal_api_pipeline:
  stages:
    - name: "build"
      jobs:
        - name: "compile"
          script: "npm install && npm run build"
          artifacts:
            paths:
              - "dist/"
            expire_in: "1 week"
    
    - name: "test"
      jobs:
        - name: "unit-tests"
          script: "npm run test:unit"
          dependencies:
            - "build"
        
        - name: "integration-tests"
          script: "npm run test:integration"
          dependencies:
            - "build"
        
        - name: "security-scan"
          script: "npm audit --audit-level high"
          dependencies:
            - "build"
    
    - name: "deploy-staging"
      when: "manual"
      jobs:
        - name: "staging-deployment"
          script: |
            docker build -t customer-portal-api:${CI_COMMIT_SHA} .
            docker push registry.example.com/customer-portal-api:${CI_COMMIT_SHA}
            kubectl set image deployment/customer-portal-api api=registry.example.com/customer-portal-api:${CI_COMMIT_SHA} -n staging
          environment:
            name: staging
            url: https://staging.customer-portal.example.com
    
    - name: "test-staging"
      jobs:
        - name: "e2e-tests"
          script: "npm run test:e2e -- --env staging"
          dependencies:
            - "deploy-staging"
    
    - name: "deploy-production"
      when: "manual"
      rules:
        - if: '$CI_COMMIT_BRANCH == "main"'
      jobs:
        - name: "production-deployment"
          script: |
            kubectl set image deployment/customer-portal-api api=registry.example.com/customer-portal-api:${CI_COMMIT_SHA} -n production
            kubectl rollout status deployment/customer-portal-api -n production
          environment:
            name: production
            url: https://api.customer-portal.example.com

  variables:
    NODE_VERSION: "18.17.0"
    DOCKER_REGISTRY: "registry.example.com"
    KUBE_NAMESPACE: "customer-portal"

  cache:
    paths:
      - node_modules/

  before_script:
    - apt-get update -qq && apt-get install -y -qq git curl
    - curl -sL https://deb.nodesource.com/setup_$NODE_VERSION | bash -
    - apt-get install -y nodejs

# Standard Operating Procedures

## Deployment Process

### Pre-Deployment Checklist
- [ ] Code review completed and approved
- [ ] Automated tests passing (unit, integration, security)
- [ ] Performance tests completed
- [ ] Security scanning passed
- [ ] Database migrations reviewed
- [ ] Rollback plan prepared
- [ ] Stakeholder notification sent

### Deployment Steps
1. **Environment Preparation**
   - Verify staging environment health
   - Check monitoring and alerting systems
   - Confirm team availability for deployment

2. **Staging Deployment**
   - Execute automated deployment pipeline
   - Run smoke tests
   - Perform manual verification
   - Document any issues or anomalies

3. **Production Deployment**
   - Execute production deployment pipeline
   - Monitor system health during deployment
   - Verify application functionality
   - Update documentation and runbooks

4. **Post-Deployment Verification**
   - Confirm all services are healthy
   - Verify key business metrics
   - Update deployment records
   - Communicate completion to stakeholders

### Rollback Procedures
- **Immediate Rollback**: If critical issues detected within 15 minutes
- **Planned Rollback**: If issues detected after initial period
- **Documentation**: All rollbacks must be documented with root cause analysis

## Incident Response Process

### Incident Classification
- **Level 1**: Minor impact, internal team aware
- **Level 2**: Moderate impact, customers affected
- **Level 3**: Major impact, widespread outage
- **Level 4**: Critical impact, business-threatening

### Response Timeline
- **Level 1**: Response within 4 hours
- **Level 2**: Response within 1 hour
- **Level 3**: Response within 15 minutes
- **Level 4**: Response within 5 minutes

### Communication Protocol
- **Initial Notification**: Within 15 minutes of detection
- **Status Updates**: Every 30 minutes during incident
- **Resolution Communication**: Within 30 minutes of resolution
- **Post-Incident Review**: Within 24 hours of resolution

{
  "dashboard": {
    "title": "DevOps KPI Dashboard",
    "uid": "devops-kpi-dashboard",
    "tags": ["devops", "kpi", "metrics"],
    "timezone": "browser",
    "panels": [
      {
        "id": 1,
        "title": "Deployment Frequency",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(increase(deployments_total[1d]))",
            "legendFormat": "Daily Deployments"
          }
        ],
        "unit": "short",
        "gridPos": {"h": 8, "w": 6, "x": 0, "y": 0}
      },
      {
        "id": 2,
        "title": "Lead Time for Changes",
        "type": "timeseries",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, sum(rate(lead_time_seconds_bucket[5m])) by (le))",
            "legendFormat": "P95 Lead Time"
          }
        ],
        "unit": "s",
        "gridPos": {"h": 8, "w": 6, "x": 6, "y": 0}
      },
      {
        "id": 3,
        "title": "Change Failure Rate",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(deployment_failures_total[1h])) / sum(rate(deployments_total[1h])) * 100",
            "legendFormat": "Failure Rate (%)"
          }
        ],
        "unit": "percent",
        "gridPos": {"h": 8, "w": 6, "x": 12, "y": 0}
      },
      {
        "id": 4,
        "title": "Mean Time to Recovery",
        "type": "timeseries",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, sum(rate(time_to_recovery_seconds_bucket[5m])) by (le))",
            "legendFormat": "P95 MTTR"
          }
        ],
        "unit": "s",
        "gridPos": {"h": 8, "w": 6, "x": 18, "y": 0}
      },
      {
        "id": 5,
        "title": "Application Performance",
        "type": "timeseries",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, handler))",
            "legendFormat": "{{handler}} P95"
          }
        ],
        "unit": "s",
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 8}
      },
      {
        "id": 6,
        "title": "System Health",
        "type": "stat",
        "targets": [
          {
            "expr": "count(up == 1) / count(up) * 100",
            "legendFormat": "System Availability"
          }
        ],
        "unit": "percent",
        "colorMode": "value",
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 8}
      }
    ],
    "time": {
      "from": "now-7d",
      "to": "now"
    },
    "refresh": "30s"
  }
}

# rollout-strategy.yaml
implementation_phases:
  phase_1_core_teams:
    timeline: "Months 13-15"
    teams_included:
      - "Platform Engineering Team"
      - "Security Team"
      - "Infrastructure Team"
    implementation_approach:
      - "Direct consultation and support"
      - "Dedicated DevOps engineer assignment"
      - "Accelerated training and onboarding"
    success_metrics:
      - "100% of core teams on DevOps platform"
      - "Established best practices documentation"
      - "Cross-team collaboration improved"
  
  phase_2_product_teams:
    timeline: "Months 16-17"
    teams_included:
      - "Product Development Teams (6-8 teams)"
      - "Quality Assurance Teams"
      - "Business Analyst Teams"
    implementation_approach:
      - "Self-service platform adoption"
      - "Peer mentoring and support"
      - "Standardized training programs"
    success_metrics:
      - "80% of product teams using platform"
      - "Reduced onboarding time for new teams"
      - "Consistent practices across teams"
  
  phase_3_enterprise_wide:
    timeline: "Month 18"
    teams_included:
      - "Remaining development teams"
      - "Data engineering teams"
      - "Analytics teams"
    implementation_approach:
      - "Mandatory adoption policy"
      - "Enforcement through governance"
      - "Continuous improvement and optimization"
    success_metrics:
      - "95% of eligible teams on platform"
      - "Standardized metrics across organization"
      - "Demonstrated ROI achievement"

scaling_considerations:
  infrastructure_scaling:
    - "Platform capacity planning"
    - "Resource allocation optimization"
    - "Performance monitoring and tuning"
    - "Disaster recovery and backup strategies"
  
  process_scaling:
    - "Governance and compliance automation"
    - "Cross-team coordination mechanisms"
    - "Change management and approval processes"
    - "Quality assurance and testing standards"
  
  cultural_scaling:
    - "Organization-wide culture change initiatives"
    - "Leadership development and coaching"
    - "Recognition and reward systems"
    - "Continuous learning and development programs"

# platform-services.py
from flask import Flask, jsonify, request
from typing import Dict, Any
import logging
from datetime import datetime
import uuid

app = Flask(__name__)
logging.basicConfig(level=logging.INFO)

class PlatformServices:
    def __init__(self):
        self.services = {}
        self.deployments = {}
        self.events = []
    
    def create_service(self, service_config: Dict[str, Any]) -> Dict[str, Any]:
        """
        Create a new service with automated infrastructure
        """
        service_id = str(uuid.uuid4())
        
        # Validate service configuration
        validation_errors = self.validate_service_config(service_config)
        if validation_errors:
            return {"error": "Validation failed", "details": validation_errors}, 400
        
        # Generate infrastructure as code
        iac_config = self.generate_iac_config(service_config)
        
        # Deploy infrastructure
        deployment_result = self.deploy_infrastructure(iac_config)
        
        # Create service record
        service_record = {
            "id": service_id,
            "name": service_config["name"],
            "config": service_config,
            "iac_config": iac_config,
            "deployment_status": deployment_result["status"],
            "created_at": datetime.utcnow().isoformat(),
            "owner": service_config.get("owner", "unknown"),
            "environment": service_config.get("environment", "development")
        }
        
        self.services[service_id] = service_record
        
        # Log event
        self.log_event("service_created", service_id, service_config)
        
        return {"service": service_record, "deployment": deployment_result}, 201
    
    def validate_service_config(self, config: Dict[str, Any]) -> list:
        """
        Validate service configuration against organization standards
        """
        errors = []
        
        # Required fields
        required_fields = ["name", "runtime", "cpu", "memory"]
        for field in required_fields:
            if field not in config:
                errors.append(f"Missing required field: {field}")
        
        # Validate naming conventions
        if "name" in config:
            if not config["name"].islower() or not config["name"].replace("-", "").replace("_", "").isalnum():
                errors.append("Service name must be lowercase alphanumeric with dashes or underscores")
        
        # Validate resource constraints
        if "cpu" in config:
            cpu_value = config["cpu"]
            if isinstance(cpu_value, str):
                cpu_value = cpu_value.replace("m", "")
            if float(cpu_value) > 16000:  # Max 16 cores
                errors.append("CPU request exceeds maximum allowed (16 cores)")
        
        if "memory" in config:
            mem_value = config["memory"]
            if isinstance(mem_value, str):
                if mem_value.endswith("Gi"):
                    mem_value = float(mem_value.replace("Gi", "")) * 1024
                elif mem_value.endswith("Mi"):
                    mem_value = float(mem_value.replace("Mi", ""))
                else:
                    mem_value = float(mem_value)
            
            if mem_value > 65536:  # Max 64GB
                errors.append("Memory request exceeds maximum allowed (64GB)")
        
        return errors
    
    def generate_iac_config(self, service_config: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate Infrastructure as Code configuration for the service
        """
        return {
            "kubernetes": {
                "deployment": {
                    "apiVersion": "apps/v1",
                    "kind": "Deployment",
                    "metadata": {
                        "name": service_config["name"],
                        "labels": {
                            "app": service_config["name"],
                            "environment": service_config.get("environment", "development")
                        }
                    },
                    "spec": {
                        "replicas": service_config.get("replicas", 1),
                        "selector": {
                            "matchLabels": {
                                "app": service_config["name"]
                            }
                        },
                        "template": {
                            "metadata": {
                                "labels": {
                                    "app": service_config["name"]
                                }
                            },
                            "spec": {
                                "containers": [
                                    {
                                        "name": service_config["name"],
                                        "image": service_config.get("image", f"{service_config['name']}:latest"),
                                        "resources": {
                                            "requests": {
                                                "cpu": service_config["cpu"],
                                                "memory": service_config["memory"]
                                            },
                                            "limits": {
                                                "cpu": service_config.get("cpu_limit", service_config["cpu"]),
                                                "memory": service_config.get("memory_limit", service_config["memory"])
                                            }
                                        },
                                        "env": service_config.get("environment_variables", []),
                                        "ports": [
                                            {
                                                "containerPort": service_config.get("port", 8080),
                                                "name": "http"
                                            }
                                        ]
                                    }
                                ]
                            }
                        }
                    }
                },
                "service": {
                    "apiVersion": "v1",
                    "kind": "Service",
                    "metadata": {
                        "name": f"{service_config['name']}-svc"
                    },
                    "spec": {
                        "selector": {
                            "app": service_config["name"]
                        },
                        "ports": [
                            {
                                "port": 80,
                                "targetPort": service_config.get("port", 8080),
                                "name": "http"
                            }
                        ],
                        "type": "ClusterIP"
                    }
                }
            },
            "monitoring": {
                "service_monitor": {
                    "apiVersion": "monitoring.coreos.com/v1",
                    "kind": "ServiceMonitor",
                    "metadata": {
                        "name": f"{service_config['name']}-monitor"
                    },
                    "spec": {
                        "selector": {
                            "matchLabels": {
                                "app": service_config["name"]
                            }
                        },
                        "endpoints": [
                            {
                                "port": "http",
                                "path": "/metrics"
                            }
                        ]
                    }
                }
            }
        }
    
    def deploy_infrastructure(self, iac_config: Dict[str, Any]) -> Dict[str, Any]:
        """
        Deploy infrastructure using the generated IaC configuration
        """
        # In a real implementation, this would interact with Kubernetes API
        # For this example, we'll simulate the deployment
        import time
        time.sleep(1)  # Simulate deployment time
        
        return {
            "status": "success",
            "deployment_id": str(uuid.uuid4()),
            "timestamp": datetime.utcnow().isoformat(),
            "resources_created": [
                "Deployment",
                "Service",
                "ServiceMonitor"
            ]
        }
    
    def log_event(self, event_type: str, service_id: str, details: Dict[str, Any]):
        """
        Log platform events for monitoring and analytics
        """
        event = {
            "timestamp": datetime.utcnow().isoformat(),
            "event_type": event_type,
            "service_id": service_id,
            "details": details
        }
        self.events.append(event)
        logging.info(f"Event logged: {event_type} for service {service_id}")

platform = PlatformServices()

@app.route('/api/v1/services', methods=['POST'])
def create_service():
    service_config = request.json
    result, status_code = platform.create_service(service_config)
    return jsonify(result), status_code

@app.route('/api/v1/services/<service_id>', methods=['GET'])
def get_service(service_id):
    service = platform.services.get(service_id)
    if not service:
        return jsonify({"error": "Service not found"}), 404
    return jsonify({"service": service}), 200

@app.route('/api/v1/events', methods=['GET'])
def get_events():
    return jsonify({"events": platform.events[-100:]}), 200

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)

# policy-as-code.yaml
opa_policies:
  kubernetes_policies:
    - name: "restrict_privileged_containers"
      description: "Prevent privileged containers in production"
      enforcement: "production"
      rego_policy: |
        package kubernetes.admission
        
        violation[msg] {
          input.request.kind.kind == "Pod"
          container := input.request.object.spec.containers[_]
          container.securityContext.privileged == true
          input.request.object.metadata.namespace == "production"
          msg := sprintf("Privileged container not allowed in production: %v", [container.name])
        }
    
    - name: "require_resource_limits"
      description: "Require resource limits for all containers"
      enforcement: "all"
      rego_policy: |
        package kubernetes.admission
        
        violation[msg] {
          input.request.kind.kind == "Pod"
          container := input.request.object.spec.containers[_]
          not container.resources.limits.cpu
          not container.resources.limits.memory
          msg := sprintf("Resource limits required for container: %v", [container.name])
        }
    
    - name: "restrict_node_ports"
      description: "Restrict node port usage"
      enforcement: "all"
      rego_policy: |
        package kubernetes.admission
        
        violation[msg] {
          input.request.kind.kind == "Service"
          input.request.object.spec.type == "NodePort"
          msg := "NodePort services are not allowed"
        }

  security_policies:
    - name: "scan_images_before_deployment"
      description: "Ensure all images are scanned before deployment"
      enforcement: "all"
      rego_policy: |
        package security.admission
        
        violation[msg] {
          input.deployment.spec.template.spec.containers[_].image == image
          not input.security_scans[image].passed
          msg := sprintf("Image not scanned or failed scan: %v", [image])
        }
    
    - name: "require_secure_config"
      description: "Require secure configuration for applications"
      enforcement: "production"
      rego_policy: |
        package security.admission
        
        violation[msg] {
          input.app_config.security.enabled == false
          input.environment == "production"
          msg := "Security must be enabled for production applications"
        }

gatekeeper_constraints:
  allowed_registries:
    apiVersion: constraints.gatekeeper.sh/v1beta1
    kind: K8sAllowedRepos
    metadata:
      name: allowed-registries
    spec:
      match:
        kinds:
          - apiGroups: [""]
            kinds: ["Pod"]
      parameters:
        repos:
          - "registry.company.com/"
          - "docker.io/library/"
  
  privileged_containers:
    apiVersion: constraints.gatekeeper.sh/v1beta1
    kind: K8sPSPPrivilegedContainer
    metadata:
      name: privileged-containers
    spec:
      match:
        kinds:
          - apiGroups: [""]
            kinds: ["Pod"]
        excludedNamespaces:
          - kube-system
          - gatekeeper-system

compliance_reporting:
  automated_reports:
    - name: "weekly_compliance_report"
      schedule: "0 0 * * 1"  # Every Monday at midnight
      recipients: ["[email protected]", "[email protected]"]
      content:
        - "Policy violation summary"
        - "Compliance score by team"
        - "Security scan results"
        - "Recommended actions"
    
    - name: "monthly_audit_report"
      schedule: "0 0 1 * *"  # First day of every month
      recipients: ["[email protected]", "[email protected]"]
      content:
        - "Overall compliance status"
        - "Trend analysis"
        - "Risk assessment"
        - "Improvement recommendations"

# advanced-practices.yaml
progressive_delivery_strategies:
  canary_deployment:
    tool: "Argo Rollouts"
    configuration:
      steps:
        - setWeight: 10
        - pause: {duration: 2m}
        - setWeight: 20
        - pause: {duration: 2m}
        - setWeight: 40
        - pause: {duration: 2m}
        - setWeight: 60
        - pause: {duration: 2m}
        - setWeight: 80
        - pause: {duration: 2m}
        - setWeight: 100
      analysis:
        templates:
          - templateName: success-rate
        args:
          - name: service-name
            value: my-app-canary
    
    monitoring_integration:
      success_rate_threshold: 95
      error_rate_threshold: 5
      rollback_on_failure: true
      analysis_duration: "5m"
  
  blue_green_deployment:
    tool: "Flagger"
    configuration:
      promotion_strategy:
        when_ready: "automatically"
        health_check: "comprehensive"
        rollback_on_failure: true
      verification_steps:
        - name: "health_check"
          command: "curl -f http://app-url/health || exit 1"
        - name: "functional_test"
          command: "npm run test:functional -- --env staging"
        - name: "performance_test"
          command: "npm run test:performance -- --env staging"
    
    rollback_procedures:
      automatic: true
      conditions:
        - "health_check_failure"
        - "performance_degradation"
        - "error_rate_exceeds_threshold"
      recovery_time: "< 5 minutes"

chaos_engineering_program:
  chaos_mesh_configuration:
    experiments:
      - name: "pod_failure_simulation"
        schedule: "0 2 * * 1-5"  # Weekdays at 2 AM
        target:
          kind: "Pod"
          selector:
            namespaces:
              - "production"
            labelSelectors:
              app: "critical-service"
        duration: "5m"
        chaos_type: "PodChaos"
        action: "pod-failure"
      
      - name: "network_latency_simulation"
        schedule: "0 3 * * 1-5"  # Weekdays at 3 AM
        target:
          kind: "Pod"
          selector:
            namespaces:
              - "production"
        duration: "10m"
        chaos_type: "NetworkChaos"
        action: "delay"
        delay_config:
          latency: "1000ms"
          correlation: "100"
      
      - name: "cpu_hog_simulation"
        schedule: "0 4 * * 1-5"  # Weekdays at 4 AM
        target:
          kind: "Pod"
          selector:
            namespaces:
              - "production"
            labelSelectors:
              app: "high_traffic_service"
        duration: "5m"
        chaos_type: "StressChaos"
        stressors:
          cpu:
            workers: 4
            load: 80
    
    monitoring_and_alerting:
      baseline_metrics:
        - "response_time_p95"
        - "error_rate"
        - "throughput"
        - "availability"
      alert_conditions:
        - "baseline_deviation > 20%"
        - "error_rate > 5%"
        - "availability < 99%"
      incident_response:
        automatic_detection: true
        response_time: "< 2 minutes"
        escalation_path: "on_call_engineer -> manager -> director"

  litmus_chaos_experiments:
    experiment_templates:
      - name: "application_crash"
        description: "Simulate application crash to test restart policies"
        components:
          appinfo:
            appns: "production"
            applabel: "app=web-app"
            appkind: "deployment"
          chaosengine:
            annotationcheck: "false"
            engineState: "active"
            appinfo:
              appns: "production"
              applabel: "app=web-app"
              appkind: "deployment"
            chaosServiceAccount: "litmus-admin"
            experiments:
              - name: "pod-delete"
                spec:
                  components:
                    env:
                      - name: "APP_POD"
                        value: ""
                      - name: "TOTAL_CHAOS_DURATION"
                        value: "60"
                      - name: "CHAOS_INTERVAL"
                        value: "10"
                      - name: "FORCE"
                        value: "false"

# aiops-implementation.py
import pandas as pd
import numpy as np
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import joblib
from datetime import datetime, timedelta
import json
from typing import Dict, List, Tuple
import asyncio
import aiohttp

class AIOpsPlatform:
    def __init__(self):
        self.anomaly_detector = IsolationForest(contamination=0.1, random_state=42)
        self.root_cause_analyzer = RandomForestClassifier(n_estimators=100, random_state=42)
        self.scaler = StandardScaler()
        self.models_trained = False
        
    def prepare_training_data(self, metrics_data: List[Dict]) -> Tuple[np.ndarray, np.ndarray]:
        """
        Prepare training data from metrics for ML models
        """
        df = pd.DataFrame(metrics_data)
        
        # Feature engineering
        features = df[['cpu_usage', 'memory_usage', 'disk_usage', 
                      'network_in', 'network_out', 'request_rate', 
                      'error_rate', 'response_time']].fillna(0)
        
        # Create anomaly labels (manually labeled for training)
        # In practice, this would come from historical incident data
        anomaly_labels = np.random.choice([0, 1], size=len(df), p=[0.9, 0.1])
        
        return self.scaler.fit_transform(features), anomaly_labels
    
    def train_models(self, training_data: List[Dict]):
        """
        Train ML models for anomaly detection and root cause analysis
        """
        X, y_anomaly = self.prepare_training_data(training_data)
        
        # Train anomaly detector
        self.anomaly_detector.fit(X)
        
        # For root cause analysis, we'd need labeled root cause data
        # This is a simplified example
        y_cause = np.random.choice(range(5), size=len(X))  # 5 different root causes
        self.root_cause_analyzer.fit(X, y_cause)
        
        self.models_trained = True
        
        # Save models
        joblib.dump(self.anomaly_detector, 'anomaly_detector.pkl')
        joblib.dump(self.root_cause_analyzer, 'root_cause_analyzer.pkl')
        joblib.dump(self.scaler, 'scaler.pkl')
    
    def detect_anomalies(self, current_metrics: List[Dict]) -> List[Dict]:
        """
        Detect anomalies in current metrics using trained model
        """
        if not self.models_trained:
            raise ValueError("Models must be trained before detecting anomalies")
        
        df = pd.DataFrame(current_metrics)
        features = df[['cpu_usage', 'memory_usage', 'disk_usage', 
                      'network_in', 'network_out', 'request_rate', 
                      'error_rate', 'response_time']].fillna(0)
        
        X_scaled = self.scaler.transform(features)
        anomaly_predictions = self.anomaly_detector.predict(X_scaled)
        anomaly_scores = self.anomaly_detector.decision_function(X_scaled)
        
        results = []
        for i, (pred, score) in enumerate(zip(anomaly_predictions, anomaly_scores)):
            is_anomaly = pred == -1
            results.append({
                'timestamp': current_metrics[i]['timestamp'],
                'is_anomaly': is_anomaly,
                'anomaly_score': float(score),
                'severity': 'high' if is_anomaly and abs(score) > 0.5 else 'medium' if is_anomaly else 'normal',
                'metrics': current_metrics[i]
            })
        
        return results
    
    async def analyze_root_cause(self, anomaly_data: List[Dict]) -> List[Dict]:
        """
        Analyze root cause of detected anomalies
        """
        if not self.models_trained:
            raise ValueError("Models must be trained before analyzing root cause")
        
        # Prepare features for root cause analysis
        features_list = []
        for anomaly in anomaly_data:
            if anomaly['is_anomaly']:
                metrics = anomaly['metrics']
                features = [metrics['cpu_usage'], metrics['memory_usage'], 
                           metrics['disk_usage'], metrics['network_in'], 
                           metrics['network_out'], metrics['request_rate'], 
                           metrics['error_rate'], metrics['response_time']]
                features_list.append(features)
        
        if not features_list:
            return []
        
        X = np.array(features_list)
        X_scaled = self.scaler.transform(X)
        
        predictions = self.root_cause_analyzer.predict(X_scaled)
        probabilities = self.root_cause_analyzer.predict_proba(X_scaled)
        
        root_causes = [
            "High CPU Usage",
            "Memory Pressure", 
            "Disk I/O Bottleneck",
            "Network Congestion",
            "Application Logic Issue"
        ]
        
        results = []
        for i, (prediction, prob) in enumerate(zip(predictions, probabilities)):
            results.append({
                'anomaly_id': anomaly_data[i]['timestamp'],
                'predicted_cause': root_causes[prediction],
                'confidence': float(max(prob)),
                'probability_distribution': {
                    cause: float(prob_score) 
                    for cause, prob_score in zip(root_causes, prob)
                }
            })
        
        return results

class IncidentPredictionEngine:
    def __init__(self):
        self.aiops_platform = AIOpsPlatform()
        self.incident_history = []
        
    async def predict_incidents(self, current_metrics: List[Dict]) -> List[Dict]:
        """
        Predict potential incidents based on current metrics
        """
        # Detect anomalies
        anomalies = self.aiops_platform.detect_anomalies(current_metrics)
        
        # Analyze root causes for anomalies
        if any(a['is_anomaly'] for a in anomalies):
            root_causes = await self.aiops_platform.analyze_root_cause(
                [a for a in anomalies if a['is_anomaly']]
            )
        
        # Generate predictions
        predictions = []
        for anomaly in anomalies:
            if anomaly['is_anomaly']:
                prediction = {
                    'timestamp': anomaly['timestamp'],
                    'risk_level': anomaly['severity'],
                    'predicted_incident_type': 'performance_degradation',
                    'confidence': 0.8,  # Based on anomaly score
                    'recommended_action': 'increase_monitoring_frequency',
                    'affected_services': ['web-app', 'api-service']
                }
                predictions.append(prediction)
        
        return predictions
    
    async def get_real_time_recommendations(self, current_state: Dict) -> List[str]:
        """
        Provide real-time recommendations based on current system state
        """
        recommendations = []
        
        # Check for various conditions
        if current_state.get('cpu_usage', 0) > 80:
            recommendations.append("Consider scaling up CPU resources")
        
        if current_state.get('memory_usage', 0) > 85:
            recommendations.append("Investigate memory leaks or increase memory allocation")
        
        if current_state.get('error_rate', 0) > 5:
            recommendations.append("Review application logs for error patterns")
        
        if current_state.get('response_time', 0) > 2000:  # 2 seconds
            recommendations.append("Check database performance and queries")
        
        return recommendations

# Example usage
async def main():
    aiops = IncidentPredictionEngine()
    
    # Simulate current metrics data
    current_metrics = [
        {
            'timestamp': (datetime.now() - timedelta(minutes=i)).isoformat(),
            'cpu_usage': np.random.normal(70, 15) if i > 5 else np.random.normal(40, 5),
            'memory_usage': np.random.normal(65, 10),
            'disk_usage': np.random.normal(50, 8),
            'network_in': np.random.exponential(100),
            'network_out': np.random.exponential(80),
            'request_rate': np.random.poisson(100),
            'error_rate': np.random.uniform(0, 3),
            'response_time': np.random.gamma(2, 1000)
        }
        for i in range(20)
    ]
    
    # Train models with historical data (simulated)
    historical_data = current_metrics + [
        {
            'timestamp': (datetime.now() - timedelta(hours=h)).isoformat(),
            'cpu_usage': np.random.normal(40, 10),
            'memory_usage': np.random.normal(50, 12),
            'disk_usage': np.random.normal(45, 8),
            'network_in': np.random.exponential(50),
            'network_out': np.random.exponential(40),
            'request_rate': np.random.poisson(50),
            'error_rate': np.random.uniform(0, 1),
            'response_time': np.random.gamma(2, 500)
        }
        for h in range(24, 168)  # Previous week data
    ]
    
    aiops.aiops_platform.train_models(historical_data)
    
    # Predict incidents
    predictions = await aiops.predict_incidents(current_metrics)
    print("Incident Predictions:")
    for pred in predictions:
        print(f"- {pred}")
    
    # Get recommendations
    current_state = current_metrics[0]  # Most recent
    recommendations = await aiops.get_real_time_recommendations(current_state)
    print("\nReal-time Recommendations:")
    for rec in recommendations:
        print(f"- {rec}")

if __name__ == "__main__":
    asyncio.run(main())

# sustainability-plan.yaml
culture_development:
  devops_champions_program:
    selection_criteria:
      - "Technical expertise and credibility"
      - "Communication and leadership skills"
      - "Passion for DevOps transformation"
      - "Cross-team collaboration ability"
    
    responsibilities:
      - "Local DevOps advocacy and coaching"
      - "Best practice sharing and mentoring"
      - "Feedback collection and communication"
      - "Change management support"
    
    recognition_program:
      - "Quarterly champion awards"
      - "Conference speaking opportunities"
      - "Career advancement consideration"
      - "Innovation project leadership"
  
  learning_and_development:
    continuous_learning_budget:
      amount_per_employee: "$2000 annually"
      approved_expenses:
        - "Training courses and certifications"
        - "Conference attendance"
        - "Books and educational materials"
        - "Online learning platforms"
    
    internal_knowledge_sharing:
      - "Monthly tech talks"
      - "Quarterly hackathons"
      - "Cross-team rotation programs"
      - "Mentoring partnerships"
    
    external_learning:
      - "Industry conference attendance"
      - "Professional certification programs"
      - "Speaking at conferences"
      - "Open source contribution time"

process_improvement:
  continuous_improvement_framework:
    kaizen_events:
      frequency: "Monthly"
      duration: "1-3 days"
      focus_areas:
        - "Process optimization"
        - "Quality improvement"
        - "Cost reduction"
        - "Customer experience"
    
    retrospective_practices:
      team_retrospectives: "Bi-weekly"
      cross_team_retrospectives: "Monthly"
      leadership_retrospectives: "Quarterly"
    
    improvement_tracking:
      - "Idea management system"
      - "Improvement project tracking"
      - "ROI measurement for improvements"
      - "Success story sharing"

  innovation_program:
    innovation_time_allocation:
      - "20% time for experimental projects"
      - "Quarterly innovation sprints"
      - "Cross-team collaboration projects"
    
    idea_generation:
      - "Digital suggestion box"
      - "Innovation challenges and contests"
      - "Customer feedback integration"
      - "Market trend analysis"
    
    experimentation_framework:
      - "Safe-to-fail environment"
      - "Rapid prototype development"
      - "Learning from failure celebration"
      - "Innovation metrics tracking"

measurement_and_optimization:
  devops_metrics_dashboard:
    key_metrics:
      - "Deployment frequency"
      - "Lead time for changes"
      - "Change failure rate"
      - "Time to recovery"
      - "Customer satisfaction"
      - "Team satisfaction"
    
    reporting_schedule:
      daily: "Operational metrics"
      weekly: "Team performance metrics"
      monthly: "Business impact metrics"
      quarterly: "Strategic metrics"
    
    action_thresholds:
      - "Automated alerts for metric degradation"
      - "Escalation procedures for metric targets"
      - "Corrective action plans for deviations"

  feedback_loops:
    customer_feedback:
      - "Regular customer surveys"
      - "User experience monitoring"
      - "Feature usage analytics"
      - "Support ticket analysis"
    
    employee_feedback:
      - "Quarterly engagement surveys"
      - "Team health assessments"
      - "Exit interview analysis"
      - "Suggestion program"
    
    stakeholder_feedback:
      - "Executive business reviews"
      - "Board reporting"
      - "Partner feedback sessions"
      - "Market position analysis"

# future-proofing-strategy.yaml
technology_evolution:
  evaluation_framework:
    criteria:
      - "Business value and ROI"
      - "Technical fit and integration"
      - "Security and compliance alignment"
      - "Team skill requirements"
      - "Vendor support and roadmap"
      - "Community adoption and support"
    
    evaluation_process:
      - "Proof of concept development"
      - "Pilot implementation"
      - "Performance and security testing"
      - "Cost-benefit analysis"
      - "Risk assessment"
      - "Stakeholder approval"
    
    adoption_timeline:
      - "Phase 1: Research and evaluation (3 months)"
      - "Phase 2: Proof of concept (2 months)"
      - "Phase 3: Pilot implementation (3 months)"
      - "Phase 4: Organization rollout (6 months)"

  emerging_technology_monitoring:
    ai_ml_integration:
      focus_areas:
        - "Predictive analytics and forecasting"
        - "Automated incident response"
        - "Intelligent testing and quality assurance"
        - "Anomaly detection and root cause analysis"
      
      implementation_phases:
        - "Basic monitoring and alerting (Year 1)"
        - "Predictive capabilities (Year 2)"
        - "Autonomous operations (Year 3-5)"
    
    platform_engineering_advancement:
      focus_areas:
        - "Self-service capabilities"
        - "Developer experience optimization"
        - "Infrastructure abstraction"
        - "Multi-cloud management"
      
      implementation_phases:
        - "Basic platform services (Year 1)"
        - "Advanced self-service (Year 2)"
        - "Intelligent platform (Year 3-5)"
    
    security_integration:
      focus_areas:
        - "Zero trust architecture"
        - "Runtime security"
        - "Compliance automation"
        - "Threat intelligence integration"
      
      implementation_phases:
        - "Basic security scanning (Year 1)"
        - "Advanced security controls (Year 2)"
        - "Autonomous security (Year 3-5)"

skill_development_roadmap:
  current_year_focus:
    - "Advanced cloud platform skills"
    - "Infrastructure as Code mastery"
    - "Security integration expertise"
    - "Monitoring and observability skills"
  
  next_year_focus:
    - "AI/ML for operations"
    - "Platform engineering"
    - "Advanced automation"
    - "Chaos engineering"
  
  future_skills:
    - "Generative AI integration"
    - "Quantum-safe cryptography"
    - "Edge computing management"
    - "Sustainable IT practices"

  learning_pathways:
    formal_education:
      - "University partnerships"
      - "Certification programs"
      - "Bootcamp partnerships"
      - "Degree completion assistance"
    
    on_the_job_training:
      - "Mentoring programs"
      - "Rotation assignments"
      - "Stretch projects"
      - "Cross-functional teams"
    
    external_development:
      - "Conference attendance"
      - "Industry certifications"
      - "Professional memberships"
      - "Speaking opportunities"