Prerequisites for MLOps Engineering

Software Engineering Foundation

• What you Need to Know

  • Programming and Software Development

    • Python programming with object-oriented design
    • Software engineering best practices and design patterns
    • Version control with Git and collaborative development workflows
    • Resources:
      • Python for Software Development - Comprehensive Python programming guide
      • Clean Code in Python - Software engineering best practices
      • Pro Git Book - Complete Git version control guide

  • Testing and Quality Assurance

    • Unit testing, integration testing, and test-driven development
    • Code quality tools and linting practices
    • Continuous integration and automated testing pipelines
    • Resources:
      • Python Testing 101 - Comprehensive testing guide
      • pytest Documentation - Python testing framework
      • Code Quality Tools - Linting and formatting tools

  • API Development and Web Services

    • RESTful API design and implementation
    • FastAPI and Flask for ML service development
    • API documentation and OpenAPI specifications
    • Resources:
      • FastAPI Documentation - Modern Python web framework for APIs
      • REST API Design Best Practices - API design principles
      • OpenAPI Specification - API documentation standard

DevOps and Infrastructure Fundamentals

• What you Need to Know

  • Containerization and Orchestration

    • Docker fundamentals and container lifecycle management
    • Kubernetes basics and pod orchestration
    • Container registries and image management
    • Resources:
      • Docker Get Started - Official Docker tutorial
      • Kubernetes Basics - K8s fundamentals
      • Play with Docker - Interactive Docker playground

  • Cloud Platform Fundamentals

    • AWS, Azure, and Google Cloud core services
    • Cloud storage, compute, and networking concepts
    • Identity and access management (IAM) basics
    • Resources:
      • AWS Cloud Practitioner Essentials - Free AWS fundamentals
      • Microsoft Azure Fundamentals - Azure basics
      • Google Cloud Digital Leader - GCP fundamentals

  • Infrastructure as Code (IaC)

    • Terraform for multi-cloud infrastructure provisioning
    • Configuration management with Ansible
    • Infrastructure versioning and GitOps workflows
    • Resources:
      • Terraform Documentation - Infrastructure provisioning guide
      • Ansible Documentation - Configuration management platform
      • GitOps Guide - Git-based infrastructure management

Machine Learning Fundamentals

• What you Need to Know

  • ML Concepts and Algorithms

    • Supervised and unsupervised learning basics
    • Model training, validation, and evaluation concepts
    • Feature engineering and data preprocessing
    • Resources:
      • Machine Learning Crash Course - Google's ML fundamentals
      • Scikit-learn User Guide - ML library documentation
      • Elements of Statistical Learning - Free ML textbook

  • Deep Learning and Neural Networks

    • Neural network architectures and training
    • TensorFlow and PyTorch framework basics
    • Model serialization and format conversion
    • Resources:
      • Deep Learning Specialization - Andrew Ng's course (Free audit)
      • TensorFlow Tutorials - Official TF learning resources
      • PyTorch Tutorials - PyTorch framework guide

  • Data Science and Analytics

    • Data manipulation with pandas and NumPy
    • Statistical analysis and hypothesis testing
    • Data visualization and exploratory data analysis
    • Resources:
      • Python Data Science Handbook - Comprehensive data science guide
      • Pandas Documentation - Data manipulation library
      • Matplotlib Tutorials - Data visualization

Data Engineering and Pipeline Development

• What you Need to Know

  • Data Pipeline Architecture

    • ETL/ELT processes and workflow orchestration
    • Apache Airflow for pipeline scheduling
    • Data quality and validation frameworks
    • Resources:
      • Apache Airflow Documentation - Workflow orchestration platform
      • Data Pipeline Design Patterns - Data engineering resources
      • Great Expectations - Data validation framework

  • Big Data Technologies

    • Apache Spark for large-scale data processing
    • Distributed computing concepts
    • Data lake and data warehouse architectures
    • Resources:
      • Apache Spark Documentation - Distributed data processing
      • PySpark Tutorial - Spark with Python
      • Data Lake Architecture - Modern data storage patterns

  • Database Systems and SQL

    • Relational database design and SQL querying
    • NoSQL databases for ML applications
    • Data modeling and schema design
    • Resources:
      • SQL Tutorial - W3Schools - Interactive SQL learning
      • PostgreSQL Tutorial - Advanced relational database
      • MongoDB University - NoSQL database courses

Monitoring and Observability Concepts

• What you Need to Know

  • Application Performance Monitoring

    • Metrics collection and time-series analysis
    • Logging and log aggregation strategies
    • Distributed tracing and request tracking
    • Resources:
      • Prometheus Documentation - Metrics collection system
      • Grafana Tutorials - Metrics visualization
      • Jaeger Tracing - Distributed tracing system

  • Infrastructure Monitoring

    • System metrics and resource utilization
    • Alerting and notification systems
    • Incident response and troubleshooting
    • Resources:
      • Site Reliability Engineering - Google SRE practices
      • Monitoring Best Practices - SRE monitoring guide
      • Alerting Best Practices - Google alerting philosophy

Security and Compliance Fundamentals

• What you Need to Know

  • Application Security

    • Secure coding practices and vulnerability assessment
    • Authentication and authorization mechanisms
    • API security and rate limiting
    • Resources:
      • OWASP Security Guidelines - Web application security
      • API Security Best Practices - Secure API development
      • Python Security - Python security guide

  • Infrastructure Security

    • Container security and image scanning
    • Network security and firewall configuration
    • Secrets management and encryption
    • Resources:
      • Container Security - Kubernetes security guide
      • Docker Security - Container security best practices
      • HashiCorp Vault - Secrets management platform

Business and Communication Skills

• What you Need to Know

  • Technical Communication

    • Technical documentation and runbook creation
    • Stakeholder communication and requirement gathering
    • Presentation skills for technical and business audiences
    • Resources:
      • Technical Writing Course - Google - Professional technical writing
      • Documentation Best Practices - Documentation community guide
      • Business Communication - University of Pennsylvania (Free audit)

  • Project Management and Agile Practices

    • Agile methodologies and Scrum framework
    • Project planning and resource estimation
    • Cross-functional team collaboration
    • Resources:
      • Agile Methodology Guide - Comprehensive agile practices
      • Scrum Guide - Official Scrum framework
      • Project Management Basics - Google certification (Free audit)

Assessment and Readiness Check

• What you Need to Know

  • Technical Skills Validation

    • Build and containerize a simple ML API
    • Deploy application using Infrastructure as Code
    • Set up monitoring and logging for applications
    • Implement automated testing and CI/CD pipeline
    • Resources:
      • MLOps Toy Project - End-to-end MLOps project
      • Kubernetes the Hard Way - K8s deep dive
      • Terraform Examples - IaC practice projects

  • Problem-Solving and System Design

    • Design scalable ML systems and architectures
    • Troubleshoot complex distributed systems
    • Optimize system performance and resource utilization
    • Plan for high availability and disaster recovery
    • Resources:
      • System Design Primer - System design concepts
      • Designing Data-Intensive Applications - Distributed systems design
      • Site Reliability Engineering - SRE workbook

Personalized Learning Pathways

• What you Need to Know

  • For Software Engineers

    • Focus on ML fundamentals and data engineering (8-12 weeks)
    • Learn containerization and cloud platforms
    • Practice with ML frameworks and model deployment
    • Resources:
      • Machine Learning for Software Engineers - ML roadmap for developers
      • Docker for Developers - Containerization for applications
      • Cloud Native Landscape - Cloud-native technology ecosystem

  • For Data Scientists/ML Engineers

    • Focus on DevOps practices and infrastructure (10-14 weeks)
    • Learn containerization, orchestration, and monitoring
    • Practice with CI/CD pipelines and automation
    • Resources:
      • DevOps for Data Science - MLOps best practices
      • Kubernetes for ML - ML workflows on Kubernetes
      • MLOps Specialization - End-to-end MLOps

  • For DevOps Engineers

    • Focus on ML concepts and data pipelines (6-10 weeks)
    • Learn ML model lifecycle and deployment patterns
    • Practice with ML-specific monitoring and optimization
    • Resources:
      • ML for DevOps Engineers - ML production concepts
      • Model Deployment Patterns - Continuous delivery for ML
      • ML Infrastructure - MLOps tools and resources

Ready to Begin? Once you've completed these prerequisites, start with Module 1: MLOps Fundamentals to begin your MLOps Engineering journey.