What Will You Learn?
After completing this course, data scientists can confidently build, train, and tune
performant language models on AWS using SageMaker.
About This Course
Provider: AWS
Format: Online
Duration: 5.5 Hours
Target Audience: Advanced
Learning Objectives: This course introduces experienced data scientists to the challenges of building language models and the different storage, ingestion, and training options to process a large text corpus.
Course Prerequisites: AWS Technical Essentials, Amazon SageMaker Studio for Data Scientists
Assessment and Certification: NA
Instructor: AWS
Key Topics: In this course, you will learn to do the following:
Apply best practices for storing and ingesting a large amount of text data to support distributed training
Explore data parallelism and model parallelism libraries to support distributed training on SageMaker
Explain the options available on SageMaker to improve training performance, such as Amazon SageMaker Training Compiler and Elastic Fabric Adapter (EFA)
Explore large language model (LLM) optimization techniques for effective model deployment
Demonstrate how to fine-tune foundational models available on SageMaker Jumpstart
0 Comments