Core concepts of LLMOps
LLMOps takes the foundational principles of traditional MLOps and adapts them to the unique context of managing and deploying large-scale language models. This section dives into the core concepts and terminology unique to LLMOps, exploring how they differ from and build upon traditional MLOps practices.
Key LLMOps-specific terminology
Understanding LLMOps requires familiarity with certain specific terms and concepts that are referenced in the field:
- GPT: A specific type of Transformer model known for its effectiveness in generating human-like text, showcasing the capabilities of modern LLMs.
- Transformer architectures: Advanced model structures key to modern LLMs, known for their self-attention mechanisms and parallel processing capabilities.
- Attention mechanisms: Part of Transformer architectures, these mechanisms help LLMs focus on relevant parts of the input data for better language processing.
- Tokenization: The process of breaking...
