From the course: MLOps and Data Pipeline Orchestration for AI Systems
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Benefits, best practices, and considerations for LLMOps
From the course: MLOps and Data Pipeline Orchestration for AI Systems
Benefits, best practices, and considerations for LLMOps
- [Instructor] In this movie, we'll talk about benefits, best practices, and things to watch out for when you are using LLMOps. LLMOps provides structured workflows and tools to streamline the entire lifecycle of LLM powered applications, from experimentation and fine tuning to deployment and monitoring. This results in faster iteration cycles and a quicker time to market for your innovative AI solutions, leveraging large language models. LLMOps includes robust monitoring, evaluation, and feedback mechanisms, including human in the loop processes. Thus, it ensures that deployed LLM applications are reliable, generate safe and ethical outputs, and remain aligned with intended use cases and user expectations over time. LLMs by nature are computationally intensive. LLMOps practices incorporate a host of cost monitoring, infrastructure management and optimization techniques, like efficient serving frameworks and resource scheduling. And this helps them manage the significant computational…
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