Title

AWS re:Invent 2023 - Train and tune state-of-the-art ML models on Amazon SageMaker (AIM335)

Summary

• Gal Oshry, Emily Weber, and Thomas Collar presented on training and tuning machine learning models on Amazon SageMaker.
• They discussed the challenges of training large-scale models, such as hardware requirements, fault tolerance, orchestration, data management, scaling, and cost.
• Amazon SageMaker was highlighted for its ability to help with these challenges, offering features like the Create Training Job API, cluster setup, health checks, data loading options, built-in algorithms, distributed training libraries, and cost-saving measures like cluster repair and automatic shutdown of compute resources.
• New features like smart sifting of data, which can reduce training time and cost by up to 35%, and Amazon SageMaker HyperPod, which provides a managed experience with granular control, were announced.
• Emily Weber demonstrated how to fine-tune and pre-train large language models (LLMs) on SageMaker, emphasizing the ease of starting with existing Python code and scaling up to large-scale models.
• Thomas Collar shared how Toyota Research Institute uses SageMaker for a range of machine learning tasks, from small-scale experiments to large-scale training and model serving.

Insights

• The rapid advancement in machine learning, particularly in deep learning models for computer vision and natural language processing, has led to a demand for more efficient and scalable training solutions.
• Amazon SageMaker is positioned as a comprehensive platform that addresses the complexities of training large-scale models, offering a suite of tools and features that streamline the process from setup to deployment.
• The introduction of smart sifting and Amazon SageMaker HyperPod indicates AWS's commitment to innovation in the machine learning space, providing customers with new ways to optimize their training workflows and manage resources effectively.
• The use of Amazon SageMaker by Toyota Research Institute exemplifies the platform's versatility and capability to handle diverse machine learning applications, from autonomous driving to robotics.
• The session highlighted the importance of both fine-tuning pre-existing models and pre-training new models from scratch, depending on the specific data and domain requirements of the user.
• The demonstration by Emily Weber showcased the practical steps involved in pre-training a large language model on SageMaker, providing insights into the process and the benefits of using AWS infrastructure for such tasks.