⚙️ MLOps

Master machine learning operations for scalable, reliable, and automated ML systems

← Back to CS Courses

MLOps Curriculum

12
MLOps Units
~85
Operations Concepts
25+
Tools & Platforms
40+
Best Practices
1

Introduction to MLOps

Understand the principles, benefits, and challenges of machine learning operations.

  • What is MLOps?
  • DevOps vs MLOps
  • ML lifecycle challenges
  • MLOps maturity model
  • Key stakeholders
  • Business value
  • Common pitfalls
  • Success metrics
2

ML Pipeline Design

Design and implement end-to-end machine learning pipelines for production systems.

  • Pipeline architecture
  • Data ingestion
  • Feature engineering
  • Model training
  • Model validation
  • Model deployment
  • Pipeline orchestration
  • Error handling
3

Version Control for ML

Implement version control strategies for code, data, and models in ML projects.

  • Git for ML projects
  • Data versioning
  • Model versioning
  • Experiment tracking
  • Artifact management
  • Reproducibility
  • Branching strategies
  • Collaboration workflows
4

Continuous Integration for ML

Build automated testing and integration pipelines for machine learning systems.

  • CI/CD concepts
  • Automated testing
  • Data validation
  • Model testing
  • Integration testing
  • Pipeline automation
  • Quality gates
  • Feedback loops
5

Model Deployment Strategies

Learn various deployment patterns and strategies for ML models in production.

  • Deployment patterns
  • Blue-green deployment
  • Canary releases
  • A/B testing
  • Shadow deployment
  • Multi-model serving
  • Edge deployment
  • Rollback strategies
6

Monitoring and Observability

Implement comprehensive monitoring for ML systems and model performance.

  • ML monitoring strategy
  • Data drift detection
  • Model drift monitoring
  • Performance metrics
  • Business metrics
  • Alerting systems
  • Incident response
  • Observability tools
7

Infrastructure as Code

Manage ML infrastructure using code-based approaches for scalability and reliability.

  • Infrastructure concepts
  • Terraform for ML
  • CloudFormation
  • Kubernetes for ML
  • Container orchestration
  • Resource management
  • Environment consistency
  • Cost optimization
8

Feature Stores

Build and manage centralized feature stores for consistent feature engineering.

  • Feature store concepts
  • Feature engineering
  • Feature discovery
  • Feature serving
  • Feature lineage
  • Real-time features
  • Batch features
  • Popular solutions
9

Experiment Management

Track, compare, and manage ML experiments for optimal model development.

  • Experiment tracking
  • Hyperparameter tuning
  • Model comparison
  • Metadata management
  • Experiment reproducibility
  • Collaboration tools
  • MLflow
  • Weights & Biases
10

Model Governance

Implement governance frameworks for responsible and compliant ML systems.

  • Model governance framework
  • Model registry
  • Compliance requirements
  • Audit trails
  • Risk management
  • Model approval processes
  • Documentation standards
  • Ethical considerations
11

MLOps Platforms

Explore and compare popular MLOps platforms and tools for end-to-end ML workflows.

  • Platform comparison
  • Kubeflow
  • MLflow
  • AWS SageMaker
  • Google AI Platform
  • Azure ML
  • Open source tools
  • Platform selection
12

Advanced MLOps Topics

Explore advanced concepts and emerging trends in machine learning operations.

  • AutoML integration
  • Federated learning
  • Edge MLOps
  • Multi-cloud strategies
  • MLOps security
  • Continuous learning
  • Future trends
  • Career paths

Unit 1: Introduction to MLOps

Understand the principles, benefits, and challenges of machine learning operations.

What is MLOps?

Learn the definition, scope, and importance of MLOps in modern machine learning development.

Operations Automation Reliability
MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to deploy and maintain ML systems in production reliably and efficiently. It bridges the gap between model development and production deployment.
# MLOps Definition and Scope
mlops_framework = {
  "definition": "Practices for deploying and maintaining ML systems in production",
  "core_principles": {
    "automation": "Automate ML workflows from training to deployment",
    "collaboration": "Enable cross-functional team collaboration",
    "continuous_integration": "Automated testing and validation",
    "continuous_deployment": "Automated model deployment",
    "monitoring": "Continuous monitoring of model performance",
    "governance": "Model versioning and compliance"
  },
  "key_components": {
    "data_pipeline": "Automated data ingestion and preprocessing",
    "model_pipeline": "Training, validation, and deployment automation",
    "infrastructure": "Scalable and reliable compute resources",
    "monitoring": "Performance and drift detection systems",
    "governance": "Version control and compliance frameworks"
  },
  "business_benefits": [
    "Faster time to market",
    "Improved model reliability",
    "Reduced operational overhead",
    "Better collaboration",
    "Risk mitigation"
  ]
}

DevOps vs MLOps

Understand the similarities and key differences between traditional DevOps and MLOps practices.

Key Differences:
• DevOps: Focus on software delivery and infrastructure
• MLOps: Focus on data, models, and ML-specific workflows
• DevOps: Code versioning and testing
• MLOps: Code, data, and model versioning with ML-specific testing
• DevOps: Application monitoring
• MLOps: Model performance and data drift monitoring
Shared Principles:
Both DevOps and MLOps emphasize automation, continuous integration/deployment, monitoring, and collaboration. MLOps extends these principles to handle the unique challenges of machine learning systems.
# DevOps vs MLOps Comparison
comparison = {
  "shared_principles": {
    "automation": "Automate repetitive tasks",
    "ci_cd": "Continuous integration and deployment",
    "monitoring": "Continuous system monitoring",
    "collaboration": "Cross-functional team collaboration",
    "version_control": "Track changes and enable rollbacks"
  },
  "devops_focus": {
    "artifacts": ["Code", "Configuration", "Infrastructure"],
    "testing": ["Unit tests", "Integration tests", "Performance tests"],
    "deployment": ["Application deployment", "Blue-green", "Canary"],
    "monitoring": ["System metrics", "Application logs", "User behavior"]
  },
  "mlops_additions": {
    "artifacts": ["+ Data", "+ Models", "+ Feature stores"],
    "testing": ["+ Data validation", "+ Model testing", "+ A/B testing"],
    "deployment": ["+ Model serving", "+ Shadow deployment", "+ Feature flags"],
    "monitoring": ["+ Model drift", "+ Data drift", "+ Business metrics"]
  },
  "unique_challenges": [
    "Data quality and drift",
    "Model performance degradation",
    "Experimental nature of ML",
    "Reproducibility requirements",
    "Regulatory compliance"
  ]
}

MLOps Maturity Model

Assess and improve your organization's MLOps capabilities using maturity models.

Maturity Levels:
• Level 0: Manual processes, ad-hoc workflows
• Level 1: Automated training pipelines
• Level 2: Automated deployment and monitoring
• Level 3: Full automation with continuous learning
• Level 4: Advanced optimization and governance
Assessment Areas:
Evaluate your organization across data management, model development, deployment automation, monitoring capabilities, and governance practices to determine current maturity level.
# MLOps Maturity Assessment
maturity_model = {
  "level_0_manual": {
    "characteristics": ["Manual data preparation", "Notebook-based development", "Manual deployment"],
    "challenges": ["Inconsistent results", "Long deployment times", "No monitoring"],
    "next_steps": ["Automate data pipelines", "Version control", "Basic CI/CD"]
  },