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Catalog

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Explore the AWS re:Invent catalog to discover a wide variety of session types, activities, and more. Or visit our Content Types and Policies page to learn more about how to navigate our catalog and event. New content is added regularly, and all content is subject to change.

Registration

3 sessions

Builders' session
AI/ML
Compute
Cross-Industry Solutions
Generative AI
Cost Optimization
Innovation & Transformation
300 – Advanced
Academic / Researcher
Data Scientist
Developer / Engineer
MGM Grand
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon SageMaker
AWS Trainium
Tuesday, Dec 03
2:00 p.m. Tuesday, Dec 03
Large language models (LLMs) are pretrained on vast amounts of data and perform well across a variety of general-purpose tasks and benchmarks without further specialized training. In practice, however, it is common to improve the performance of a pretrained LLM by fine-tuning the model using a smaller task-specific or domain-specific dataset. In this builders’ session, learn how to use Amazon SageMaker to fine-tune a pretrained Hugging Face LLM using AWS Trainium, and then leverage the fine-tuned model for inference. You must bring your laptop to participate.
, Senior ML Specialist SA, AWS
, Sr. Solutions Architect, AWS Japan
, Sr. Solutions Architect, AWS
, Inferentia Expert, AWS
, Principal SA, Annapurna ML, AWS
- Tuesday, Dec 32:30 PM - 3:30 PM PST
  MGM Grand | Level 3 | 350
Builders' session
AI/ML
Compute
Cross-Industry Solutions
Generative AI
Cost Optimization
Innovation & Transformation
300 – Advanced
Academic / Researcher
Data Scientist
Developer / Engineer
MGM Grand
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon SageMaker
AWS Trainium
Tuesday, Dec 03
4:00 p.m. Tuesday, Dec 03
Large language models (LLMs) are pretrained on vast amounts of data and perform well across a variety of general-purpose tasks and benchmarks without further specialized training. In practice, however, it is common to improve the performance of a pretrained LLM by fine-tuning the model using a smaller task-specific or domain-specific dataset. In this builders’ session, learn how to use Amazon SageMaker to fine-tune a pretrained Hugging Face LLM using AWS Trainium, and then leverage the fine-tuned model for inference. You must bring your laptop to participate.
, Senior ML Specialist SA, AWS
, Sr. Solutions Architect, AWS Japan
, Sr. Solutions Architect, AWS
, Inferentia Expert, AWS
, Principal SA, Annapurna ML, AWS
- Tuesday, Dec 34:00 PM - 5:00 PM PST
  MGM Grand | Level 3 | 350
Builders' session
AI/ML
Compute
Cross-Industry Solutions
Generative AI
Cost Optimization
Innovation & Transformation
300 – Advanced
Academic / Researcher
Data Scientist
Developer / Engineer
MGM Grand
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon SageMaker
AWS Trainium
Tuesday, Dec 03
11:00 a.m. Tuesday, Dec 03
Large language models (LLMs) are pretrained on vast amounts of data and perform well across a variety of general-purpose tasks and benchmarks without further specialized training. In practice, however, it is common to improve the performance of a pretrained LLM by fine-tuning the model using a smaller task-specific or domain-specific dataset. In this builders’ session, learn how to use Amazon SageMaker to fine-tune a pretrained Hugging Face LLM using AWS Trainium, and then leverage the fine-tuned model for inference. You must bring your laptop to participate.
, Senior ML Specialist SA, AWS
, Sr. Solutions Architect, AWS Japan
, Sr. Solutions Architect, AWS
, Inferentia Expert, AWS
, Principal SA, Annapurna ML, AWS
- Tuesday, Dec 311:30 AM - 12:30 PM PST
  MGM Grand | Level 1 | Terrace 151