LLMOps is a sub-category within MLOps, LLMOps emphasizes the operational capabilities, tools, and infrastructure necessary to fine-tune and deploy Large Language Models (LLMs) as integrated components of a product. It acknowledges the distinct challenges and requirements of handling LLMs in comparison to traditional ML models.

LLMOps in practice

  1. Fine-tuning and Deployment: Though foundational models are enormous and computationally intensive to train from scratch, fine-tuning them for specific tasks remains crucial. LLMOps manages this fine-tuning process, ensuring optimal results.
  2. High Compute Infrastructure: Given the sheer size and complexity of LLMs, robust GPU setups capable of working in parallel are essential. LLMOps ensures the right infrastructure is in place.
  3. Inference Management: The process of generating outputs (inferences) from LLMs can involve chains of models and additional checks. This is to ensure the output meets the desired standard for the end-user.
  4. LLM-as-a-Service: Some vendors, recognizing the challenges of running these models in-house, offer LLMs as an API, effectively outsourcing the compute and operational challenges.
  5. Prompt Engineering Tools: These facilitate in-context learning, allowing for model optimization without necessarily fine-tuning the entire model.
  6. Prompt Logging, Testing, and Analytics: An emerging area in LLMOps focusing on analyzing, optimizing, and ensuring the efficacy of prompts used with LLMs.

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