Deep Learning and Neural Networks

11.

Deployment and Production

11.1.

Model Optimization for Inference

11.1.1.

Quantization Techniques

11.1.1.1.

Post-Training Quantization

11.1.1.2.

Quantization-Aware Training

11.1.1.3.

Precision Reduction Impact

11.1.2.

11.1.2.1.

Structured Pruning

11.1.2.2.

Unstructured Pruning

11.1.2.3.

Magnitude-Based Pruning

11.1.2.4.

Gradual Pruning

11.1.3.

Knowledge Distillation

11.1.3.1.

Teacher-Student Framework

11.1.3.2.

Soft Target Learning

11.1.3.3.

Model Compression

11.1.4.

Neural Architecture Optimization

11.1.4.1.

Efficient Architectures

11.1.4.2.

Hardware-Aware Design

11.2.

Model Serving Infrastructure

11.2.1.

Inference Servers

11.2.2.

API Design and Implementation

11.2.2.1.

11.2.2.2.

11.2.2.3.

GraphQL Interfaces

11.2.3.

Model Serialization

11.2.3.1.

Framework-Specific Formats

11.2.3.2.

11.2.3.3.

TensorFlow SavedModel

11.2.3.4.

PyTorch TorchScript

11.2.4.

Batch vs. Real-Time Inference

11.2.5.

Scalability Considerations

11.2.5.1.

11.2.5.2.

11.2.5.3.

Caching Strategies

11.3.

Performance Monitoring

11.3.1.

Model Performance Metrics

11.3.2.

Latency and Throughput Monitoring

11.3.3.

Resource Utilization Tracking

11.3.4.

Error Rate Analysis

11.4.

Model Maintenance

11.4.1.

Data Drift Detection

11.4.1.1.

Statistical Tests

11.4.1.2.

Distribution Monitoring

11.4.2.

Model Drift Detection

11.4.2.1.

Performance Degradation

11.4.2.2.

11.4.3.

Retraining Strategies

11.4.3.1.

Scheduled Retraining

11.4.3.2.

Trigger-Based Retraining

11.4.3.3.

Incremental Learning

11.4.4.

A/B Testing for Models

11.5.

MLOps Practices

11.5.1.

Version Control

11.5.1.1.

Model Versioning

11.5.1.2.

Data Versioning

11.5.1.3.

Experiment Tracking

11.5.2.

Continuous Integration and Deployment

11.5.2.1.

Automated Testing

11.5.2.2.

Model Validation Pipelines

11.5.2.3.

Deployment Automation

11.5.3.

Reproducibility

11.5.3.1.

Environment Management

11.5.3.2.

11.5.3.3.

Deterministic Training

11.5.4.

Collaboration and Workflow Management

11.5.4.1.

Team Collaboration Tools

11.5.4.2.

Workflow Orchestration

11.5.4.3.

Documentation Standards

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10. Advanced Topics and Specialized Architectures

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1. Foundations of Machine Learning and Neural Networks