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RAFT vs Fine-Tuning Image created by author As the use of largelanguagemodels (LLMs) grows within businesses, to automate tasks, analyse data, and engage with customers; adapting these models to specific needs (e.g., Security: Secure sensitive data with access control (role-based) and metadata.
This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call datadrift. There is only one way to identify the datadrift, by continuously monitoring your models in production.
When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support.
AR : You brought up a really good point around largelanguagemodels and I believe there’s also a question here on the same topic. So this is interesting because there’ve been so many inspiring advances with largelanguagemodels of LLMs such as LaMDA or PaLM. AR : I really like that.
AR : You brought up a really good point around largelanguagemodels and I believe there’s also a question here on the same topic. So this is interesting because there’ve been so many inspiring advances with largelanguagemodels of LLMs such as LaMDA or PaLM. AR : I really like that.
AR : You brought up a really good point around largelanguagemodels and I believe there’s also a question here on the same topic. So this is interesting because there’ve been so many inspiring advances with largelanguagemodels of LLMs such as LaMDA or PaLM. AR : I really like that.
It stores the model weights and maintains a history of model versions. A model registry is a very useful tool for organizing different model versions. In addition to the model weights, a model registry also stores metadata about the data and models. Might be useful With neptune.ai
TL;DR LLMOps involves managing the entire lifecycle of LargeLanguageModels (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. What is LargeLanguageModel Operations (LLMOps)? What the future of LLMOps looks like.
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