Machine Learning

11.

Machine Learning Operations and Deployment

11.1.

ML Lifecycle Management

11.1.1.

Problem Definition and Scoping

11.1.1.1.

Business Objectives

11.1.1.2.

Success Metrics

11.1.1.3.

Constraints and Requirements

11.1.1.4.

Feasibility Assessment

11.1.2.

11.1.2.1.

Data Collection Planning

11.1.2.2.

Data Quality Requirements

11.1.2.3.

Privacy and Compliance

11.1.2.4.

Data Governance

11.1.3.

Model Development Process

11.1.3.1.

Experimentation Framework

11.1.3.2.

Version Control for ML

11.1.3.3.

Reproducibility

11.1.3.4.

Documentation Standards

11.1.4.

Model Validation and Testing

11.1.4.1.

Validation Strategies

11.1.4.2.

11.1.4.3.

Shadow Mode Testing

11.1.4.4.

Canary Deployments

11.2.

Model Deployment

11.2.1.

Deployment Patterns

11.2.1.1.

Batch Prediction

11.2.1.2.

Real-Time Prediction

11.2.1.3.

Edge Deployment

11.2.1.4.

Streaming Prediction

11.2.2.

11.2.2.1.

11.2.2.2.

11.2.2.3.

11.2.2.4.

Serverless Functions

11.2.3.

Containerization

11.2.3.1.

Docker Containers

11.2.3.2.

Container Orchestration

11.2.3.3.

Kubernetes for ML

11.2.4.

Model Serialization

11.2.4.1.

11.2.4.2.

11.2.4.3.

TensorFlow SavedModel

11.2.4.4.

PyTorch TorchScript

11.2.5.

Scalability Considerations

11.2.5.1.

11.2.5.2.

11.2.5.3.

Caching Strategies

11.2.5.4.

Performance Optimization

11.3.

Model Monitoring and Maintenance

11.3.1.

Performance Monitoring

11.3.1.1.

Prediction Quality Metrics

11.3.1.2.

Latency Monitoring

11.3.1.3.

Throughput Monitoring

11.3.1.4.

Error Rate Tracking

11.3.2.

Data Drift Detection

11.3.2.1.

Statistical Tests

11.3.2.2.

Distribution Comparison

11.3.2.3.

11.3.2.4.

11.3.3.

Model Drift Detection

11.3.3.1.

11.3.3.2.

Covariate Shift

11.3.3.3.

Prior Probability Shift

11.3.4.

Alerting and Notification

11.3.4.1.

Threshold-Based Alerts

11.3.4.2.

Anomaly-Based Alerts

11.3.4.3.

Escalation Procedures

11.3.5.

Model Retraining

11.3.5.1.

Trigger Conditions

11.3.5.2.

Automated Retraining

11.3.5.3.

Incremental Learning

11.3.5.4.

Model Rollback Strategies

11.3.6.

Logging and Observability

11.3.6.1.

Prediction Logging

11.3.6.2.

Feature Logging

11.3.6.3.

11.3.6.4.

11.4.

Ethical AI and Responsible ML

11.4.1.

Bias and Fairness

11.4.1.1.

Sources of Bias

11.4.1.1.1.

Historical Bias

11.4.1.1.2.

Representation Bias

11.4.1.1.3.

Measurement Bias

11.4.1.1.4.

Evaluation Bias

11.4.1.2.

Fairness Metrics

11.4.1.2.1.

Demographic Parity

11.4.1.2.2.

11.4.1.2.3.

Individual Fairness

11.4.1.3.

Bias Mitigation Strategies

11.4.1.3.1.

Pre-processing Methods

11.4.1.3.2.

In-processing Methods

11.4.1.3.3.

Post-processing Methods

11.4.2.

Interpretability and Explainability

11.4.2.1.

Model-Agnostic Methods

11.4.2.1.1.

11.4.2.1.2.

11.4.2.1.3.

Permutation Importance

11.4.2.1.4.

Partial Dependence Plots

11.4.2.2.

Model-Specific Methods

11.4.2.2.1.

Decision Tree Visualization

11.4.2.2.2.

Linear Model Coefficients

11.4.2.2.3.

Attention Weights

11.4.2.3.

Global vs. Local Explanations

11.4.2.4.

Counterfactual Explanations

11.4.3.

Privacy and Security

11.4.3.1.

11.4.3.1.1.

Data Anonymization

11.4.3.1.2.

Differential Privacy

11.4.3.1.3.

Federated Learning

11.4.3.2.

11.4.3.2.1.

Adversarial Attacks

11.4.3.2.2.

Model Inversion

11.4.3.2.3.

Membership Inference

11.4.3.3.

Secure Model Deployment

11.4.3.3.1.

Encrypted Inference

11.4.3.3.2.

Secure Multi-Party Computation

11.4.4.

Regulatory Compliance

11.4.4.1.

GDPR Compliance

11.4.4.2.

Right to Explanation

11.4.4.3.

Algorithmic Accountability

11.4.4.4.

Documentation Requirements

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10. Advanced Topics and Specialized Areas

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1. Introduction to Machine Learning