LLM Production
LLM Fine-Tuning Pipelines β From Data to Deployment
Production fine-tuning requires robust infrastructure for data management, training orchestration, experiment tracking, and model validation before deployment.
- Data Pipeline β Collection, cleaning, formatting, and validation
- Training Infrastructure β Distributed training, checkpointing, and experiment tracking
- Quality Assurance β Evaluation, testing, and deployment gates
The quality of your fine-tuning pipeline determines the quality of your model.
LLM Fine-Tuning Pipelines
Fine-tuning LLMs for production requires more than running a training script. You need a robust pipeline that manages data quality, tracks experiments, handles failures, and validates models before deployment. This guide covers the end-to-end infrastructure.
Data Management
Data Collection and Curation
Data Formatting
Data Pipeline Architecture:
Data Versioning
Training Infrastructure
Distributed Training
LoRA Fine-Tuning
Experiment Tracking
Metrics to Track
| Category | Metrics |
|---|---|
| Training | Loss, learning rate, gradient norms |
| Evaluation | Perplexity, BLEU, ROUGE, task-specific metrics |
| Quality | Win rate, human preference scores |
| System | GPU utilization, throughput, memory usage |
| Cost | GPU-hours, tokens processed, cost per experiment |
Experiment Organization
Quality Assurance
Automated Evaluation
Human Evaluation
Deployment Gates
| Gate | Criteria | Tool |
|---|---|---|
| Format | Output matches expected schema | JSON validator |
| Safety | No harmful content | Safety classifier |
| Quality | Win rate > baseline | A/B evaluation |
| Latency | Meets SLO requirements | Load testing |
| Memory | Fits serving infrastructure | Memory profiling |
Practice Exercises
-
Conceptual: Explain why data quality is more important than data quantity for LLM fine-tuning. What are the failure modes of training on large but noisy datasets?
-
Mathematical: Calculate the GPU memory required to fine-tune a 13B parameter model using LoRA (rank 32) on a single A100-80GB GPU, given 4-bit quantization.
-
Practical: Design an experiment tracking system for LLM fine-tuning that supports comparison of 50+ experiments with hyperparameter search and automated best-model selection.
-
Research: Compare the cost-effectiveness of full fine-tuning versus LoRA fine-tuning for adapting a 70B model to a specialized domain.
What to Learn Next
-> LLM Versioning and Rollouts Model versioning, artifact management, and gradual rollout strategies.
-> LLM Evaluation in Production Online evaluation, user feedback loops, and quality assurance.
-> Multi-Tenant LLM Systems Tenant isolation, resource sharing, and customization at scale.
-> Cost Optimization for LLMs Token economics, caching, and batching for cost efficiency.
-> LLM Serving Architectures vLLM, TGI, TensorRT-LLM, and serving patterns for production deployments.
-> AB Testing for LLMs Experiment design, statistical significance, and canary deployments.