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Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)
Chapters

1Foundations of Fine-Tuning

2Performance and Resource Optimization

3Parameter-Efficient Fine-Tuning Methods

4Data Efficiency and Curation

5Quantization, Pruning, and Compression

6Scaling and Distributed Fine-Tuning (DeepSpeed, FSDP, ZeRO)

7Evaluation, Validation, and Monitoring

8Real-World Applications and Deployment

8.1 Domain-Specific Fine-Tuning Use Cases8.2 Deployment Pipelines and CI/CD for LLMs8.3 Inference Cost Management in Production8.4 Model Serving Options and Toolchains8.5 Observability in Production (Logs, Traces, Metrics)8.6 Safety, Compliance, and Governance in Deployment8.7 Versioning and Rollouts8.8 Multi-Tenant Deployment Considerations8.9 Localization and Multilingual Deployment8.10 Prompt Design and Developer Experience8.11 Data Refresh and Re-training Triggers8.12 Monitoring Data Pipelines in Production8.13 Model Update Strategies8.14 Canary Deployments and Rollbacks8.15 Disaster Recovery Planning

9Future of Fine-Tuning (Mixture of Experts, Retrieval-Augmented Fine-Tuning, Continual Learning)

10Practical Verification, Debugging, and Validation Pipelines

11Cost Modeling, Budgeting, and Operational Efficiency

12Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

Courses/Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)/Real-World Applications and Deployment

Real-World Applications and Deployment

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From domain adaptation to production deployment, this module covers end-to-end workflows, including serving, observability, safety, and governance in real-world use cases.

Content

14 of 15

8.14 Canary Deployments and Rollbacks

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