Generative AI

9.

Development Lifecycle and Best Practices

9.1.

Project Planning and Design

9.1.1.

Problem Definition

9.1.1.1.

Use Case Identification

9.1.1.2.

Success Metrics Definition

9.1.1.3.

Feasibility Assessment

9.1.2.

Architecture Design

9.1.2.1.

Model Selection

9.1.2.2.

Infrastructure Planning

9.1.2.3.

Scalability Considerations

9.2.

Data Management

9.2.1.

Data Collection Strategies

9.2.1.1.

Public Dataset Utilization

9.2.1.2.

Proprietary Data Gathering

9.2.1.3.

Synthetic Data Generation

9.2.2.

Data Preprocessing

9.2.2.1.

Cleaning and Filtering

9.2.2.2.

Normalization Techniques

9.2.2.3.

Augmentation Strategies

9.2.3.

Data Quality Assurance

9.2.3.1.

9.2.3.2.

Quality Metrics

9.2.3.3.

Validation Procedures

9.3.

Model Development

9.3.1.

Training Strategies

9.3.1.1.

Pretraining Approaches

9.3.1.2.

Transfer Learning

9.3.1.3.

Fine-Tuning Techniques

9.3.2.

Hyperparameter Optimization

9.3.2.1.

9.3.2.2.

9.3.2.3.

Bayesian Optimization

9.3.3.

Regularization and Validation

9.3.3.1.

Cross-Validation

9.3.3.2.

9.3.3.3.

Model Ensembling

9.4.

Evaluation and Testing

9.4.1.

Quantitative Metrics

9.4.1.1.

Perplexity for Language Models

9.4.1.2.

FID for Image Generation

9.4.1.3.

BLEU for Translation

9.4.2.

Qualitative Assessment

9.4.2.1.

Human Evaluation

9.4.2.2.

9.4.2.3.

9.4.3.

Robustness Testing

9.4.3.1.

Adversarial Examples

9.4.3.2.

Out-of-Distribution Testing

9.4.3.3.

9.5.

Deployment and Operations

9.5.1.

9.5.1.1.

API Development

9.5.1.2.

9.5.1.3.

Caching Strategies

9.5.2.

Monitoring and Maintenance

9.5.2.1.

Performance Monitoring

9.5.2.2.

Model Drift Detection

9.5.2.3.

Automated Retraining

9.5.3.

Version Control

9.5.3.1.

Model Versioning

9.5.3.2.

Experiment Tracking

9.5.3.3.

Rollback Procedures

Previous

8. Audio and Speech Generation

Go to top

Next

10. Practical Implementation Tools