| Summary | AL1. I developed a serverless training pipeline for fine-tuning LLMs, which significantly reduced training costs. |
| Situation | - The process for fine-tuning LLMs took many hours and was very costly.
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| Task | My mission was to create a streamlined and cost-effective training pipeline for fine-tuning LLMs. |
| Action | - I developed pipeline infrastructure
- I designed the serverless architecture to optimize compute costs
- I managed models and experiment tracking
- I integrated with Hugging Face to store and share trained models
- I used Weights & Biases (WandB) for comprehensive experiment tracking
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| Result | As a result, the new pipeline enabled efficient fine-tuning of LLMs. It significantly optimized compute resource utilization. Overall training costs decreased. |
| Challenge | |
| Solution | |
| Learning | I learned that the choice of architecture can significantly impact infrastructure costs. |
| Skill | AI/LLM / Serverless |