Model performance on specific use cases is a key blocker in deploying production-ready LLMs into enterprises. The biggest reason performance suffers is that available models are not trained on the company and use case-specific data. It’s estimated that as much as 40% of LLM initiatives are stalled by training data quality.
While many enterprises have a lot of internal data, it is most often not of the quality required to be used for fine-tuning language models. With SuperAnnotate and AWS, enterprises can easily build proprietary datasets for fine-tuning, dramatically improve LLM model performance, and deploy LLMs into the enterprise faster than ever.
