LLMOps vs. MLOps
Comparison between LLMops and MLOps across the various aspects are as follows:
Aspects |
LLMOps |
MLOps |
---|---|---|
Focus Area |
Large Language Models |
Entire Machine Learning |
Learning |
Often fine-tune from pre-trained models instead of starting training from scratch |
Either fine-tuned or trained from scratch depending on the purpose and necessity. |
Performance Metrics |
Specilized Language based metrics like BLEU, ROUGE and Human Feedback |
Traditional metrics like accuracy, AUC, F1 Score, Mean Absolute error and many more |
Deployment |
Streamlined using specialized tools and methodologies. |
Using version control, containerization, orchestration, and monitoring tools. |
Computational Resource |
Requires high computational power with Multi GPU and Distributed Computing |
Typically requires significant computational resources |
Prompt |
With prompt, we can get the specialized result |
Not specific to prompts |
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