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Infrastructure Automation

Infrastructure as Code for ML Systems​

  • What you Need to Know

    • Terraform for Multi-Cloud ML Infrastructure

      • ML infrastructure provisioning across AWS, Azure, and GCP
      • State management and remote backends for team collaboration
      • Module development for reusable ML infrastructure components
      • Resources:

    • CloudFormation and ARM Templates

    • Configuration Management with Ansible

      • Ansible playbooks for ML system configuration
      • Inventory management and variable handling
      • Integration with cloud platforms and container orchestration
      • Resources:

Container Orchestration and Kubernetes​

  • What you Need to Know

    • Kubernetes for ML Workloads

    • Helm for ML Application Management

      • Helm charts for ML application packaging
      • Configuration templating and environment management
      • Chart repositories and dependency management
      • Resources:

    • Operator Pattern for ML Systems

      • Custom Resource Definitions (CRDs) for ML workloads
      • Kubernetes operators for ML platform management
      • Controller patterns and reconciliation loops
      • Resources:

CI/CD Automation for ML​

  • What you Need to Know

    • GitOps for ML Infrastructure

      • Git-based infrastructure and configuration management
      • ArgoCD for continuous deployment of ML applications
      • Flux for GitOps automation and synchronization
      • Resources:

    • Pipeline as Code

      • Jenkins pipelines for ML workflow automation
      • GitHub Actions for ML CI/CD workflows
      • GitLab CI/CD for integrated DevOps
      • Resources:

    • Automated Testing and Validation

      • Infrastructure testing with Terratest
      • Container image security scanning
      • Compliance and policy validation automation
      • Resources:

Scalable ML Platform Architecture​

  • What you Need to Know

    • Multi-Tenant ML Platforms

    • Microservices Architecture for ML

      • Service decomposition and API design
      • Service mesh integration for ML microservices
      • Data consistency and transaction management
      • Resources:

    • Event-Driven Architecture

      • Event sourcing and CQRS patterns for ML systems
      • Message queues and event streaming integration
      • Saga pattern for distributed ML transactions
      • Resources:

Security Automation and Compliance​

  • What you Need to Know

    • Security Scanning and Vulnerability Management

      • Automated container image scanning
      • Infrastructure security scanning with tools
      • Dependency vulnerability monitoring
      • Resources:

    • Policy as Code and Compliance Automation

      • Open Policy Agent (OPA) for policy enforcement
      • Compliance scanning and reporting automation
      • Security policy validation in CI/CD pipelines
      • Resources:

    • Secrets Management Automation

      • HashiCorp Vault for secrets management
      • Kubernetes secrets and external secrets operators
      • Automated secret rotation and lifecycle management
      • Resources:

Advanced Automation Patterns​

  • What you Need to Know

    • Chaos Engineering for ML Systems

      • Fault injection and resilience testing
      • ML system failure simulation and recovery
      • Chaos engineering tools and frameworks
      • Resources:

    • Auto-Remediation and Self-Healing

    • Infrastructure Optimization Automation

Platform Engineering and Developer Experience​

  • What you Need to Know

    • Internal Developer Platform (IDP)

    • Workflow Automation and Productivity

    • Documentation Automation

      • Automated API documentation generation
      • Infrastructure documentation and diagrams
      • Runbook and procedure automation
      • Resources:

Enterprise MLOps and Governance​

  • What you Need to Know

    • ML Governance and Compliance

    • Enterprise Integration Patterns

    • Change Management and Organizational Adoption

      • MLOps transformation strategies
      • Training and enablement programs
      • Cultural change and best practice adoption
      • Resources:

Congratulations! You have completed the comprehensive MLOps Engineering learning path. You now possess the advanced skills to design, implement, and manage production-scale ML infrastructure and automation systems. Continue your journey by staying current with emerging MLOps technologies, contributing to open-source platforms, and leading ML infrastructure transformation initiatives!