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In the era of AI, fine-tuning large language models (LLMs) for specific tasks has become essential for delivering high-quality, tailored solutions. This workshop will guide you through the process of building end-to-end workflows for LLMs at scale, focusing on MLOps best practices and leveraging Anyscale's LLMForge for fine-tuning.
You'll start by fine-tuning a large language model for a specific functional representation task, using Ray Data for batch evaluation of the fine-tuned model. You'll also learn to deploy the model using Ray Serve, ensuring efficient and scalable deployment. The workshop will conclude with a deep dive into adopting MLOps best practices, including automated retraining and evaluation to maintain and improve model performance.
By the end of this session, you'll have a comprehensive understanding of how to manage the full lifecycle of LLM workflows, from fine-tuning and evaluation to deployment and continuous improvement. You'll gain practical skills to implement scalable, efficient, and automated workflows for large language models, ensuring they remain accurate and effective over time.
Prerequisites:
Familiarity with MLOps and LLM use cases.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Train
Ray Serve
You'll start by fine-tuning a large language model for a specific functional representation task, using Ray Data for batch evaluation of the fine-tuned model. You'll also learn to deploy the model using Ray Serve, ensuring efficient and scalable deployment. The workshop will conclude with a deep dive into adopting MLOps best practices, including automated retraining and evaluation to maintain and improve model performance.
By the end of this session, you'll have a comprehensive understanding of how to manage the full lifecycle of LLM workflows, from fine-tuning and evaluation to deployment and continuous improvement. You'll gain practical skills to implement scalable, efficient, and automated workflows for large language models, ensuring they remain accurate and effective over time.
Prerequisites:
Familiarity with MLOps and LLM use cases.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Train
Ray Serve
