LLMops: what's beyond ChatGPT and how you can deploy it
summary
Overview of LLMops beyond ChatGPT: explains basic ML terminology, reviews classic MLOps principles, and examines why large language models are powerful. Describes the evolution of language generation and practical options for deploying LLM-based systems.
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chapters (16)
- 0:00Introduction
- 1:30LLM market overview
- 4:20Model lifecycle in MLOps
- 9:22Audio Insights product
- 10:49Model data protection product
- 11:54Model lifecycle pipeline
- 13:46When to use LLMs
- 16:58Open vs closed models
- 19:09Hardware licenses requirements
- 21:18LLaMA models parameters
- 24:29Quantization optimization
- 27:48Fine-tuning with LoRA
- 33:05Vector databases
- 35:34LChain framework
- 36:39Model inference changes
- 38:02Q&A
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