Key Components of LLMOps
- Data Management: Ensuring the organization, accuracy, and integrity of data throughout its lifecycle.
- Architectural Design: Developing a robust framework that supports the scalability and integration needs of the system.
- Deployment: Efficiently rolling out models or systems to production environments.
- Data Privacy & Protection: Safeguarding sensitive information against unauthorized access and ensuring compliance with legal regulations.
- Ethics & Fairnness: Addressing and mitigating biases, ensuring transparency, and maintaining fairness in data usage and algorithmic decision-making.
What is LLMOps (Large Language Model Operations)?
LLMOps involves the strategies and techniques for overseeing the lifespan of large language models (LLMs) in operational environments. LLMOps ensure that LLMs are efficiently utilized for various natural language processing tasks, from fine-tuning to deployment and ongoing maintenance, in order to effectively fulfill the demand.
Table of Content
- What is LLMOps?
- Why we need LLMOps?
- Key Components of LLMOps:
- LLMOps vs. MLOps
- LLMOps Lifecycle
- LLMOPS : Pros and Cons
- Importance of LLMOps
- Future of LLMOps
- Conclusion
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