ing the significant operational costs associated with LLM training and serving. * Detect data and concept drift specific to language models. * Monitor LLM output quality, including technical approaches for identifying issues like hallucinations or undesirable content patterns. * Establish feedback loops to gather data for model improvement and retraining triggers. * Utilize logging and observability tools effectively within an LLMOps context. We will cover the tools and techniques needed to gain visibility into your model's behavior, address issues proactively, and ensure its continued value over time.18:T5dd,

Training and fine-tuning models with billions of parameters requires specialized operational practices distinct from standard machine learning workflows. The scale introduces significant challenges in computation, memory management, and coordination across potentially hundreds of accelerators.

This chapter focuses on the operational aspects of managing these large-scale training processes. You will learn how to:

Orchestrate and manage distributed training jobs efficiently.
Implement different parallelism strategies, including data parallelism, tensor parallelism, and pipeline parallelism, to handle models that exceed single device memory. For instance, understanding how to split model layers across multiple GPUs (pipeline parallelism) or individual layer computations across devices (tensor parallelism) is key.
Utilize specialized frameworks like DeepSpeed and Megatron-LM designed to simplify and optimize large model training.
Integrate parameter-efficient fine-tuning (PEFT) techniques, such as LoRA, into operational pipelines for adapting models cost-effectively.
Adapt experiment tracking and checkpointing methods to handle the scale and duration of large training runs, ensuring reproducibility and fault tolerance.

By the end of this chapter, you will understand the core techniques and operational considerations for successfull

Large Model Training and Fine-tuning Operations

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