Aim: Improve the performance of machine learning pipelines for LLM/NLP models.

Description: Enhance the performance of machine learning pipelines for large language models (LLMs) and natural language processing (NLP) tasks. Focus on optimising speed, accuracy, and resource utilisation.

Objectives:

• Analyse current ML pipelines to identify bottlenecks.
• Implement optimisation techniques for data preprocessing and training.
• Experiment with hyper-parameter tuning and model architecture refinements.

Deliverables:

• Updated ML pipelines with performance improvements.
• Performance metrics report comparing old and new pipelines.
• Documentation for future optimisations.

Outcome: Faster and more accurate ML pipelines that enable efficient handling of LLM/NLP tasks, leading to better results in less time.