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Amazon SageMaker JumpStart is a machine learning (ML) hub with foundation models, built-in algorithms, and prebuilt ML solutions. In this foundations course, you will learn how to discover foundation models that meet your requirements and deploy them using the console. Through a guided tutorial consisting of a narrated video, step-by-step instructions, and transcript, you will learn how to apply filters to identify model candidates for your use case.
- Course level: Intermediate
- Duration: 1 hour
Activities
This course includes demonstrations, videos, and knowledge check questions.
Course objectives
In this course, you will learn to do the following:
- Search the vast catalog of foundation models including large language models (LLMs) in the SageMaker JumpStart model hub, and select the right model for your use case.
- Deploy, fine-tune, and evaluate models using the SageMaker JumpStart console.
- Use deployment parameters and select instance types.
- Create generative images using text prompts.
- Cleanup the SageMaker resources you have created during demonstrations.
Intended Audience:
This course is intended for the following roles:
- Data scientists
- Machine learning engineers
Prerequisites
We recommend that attendees of this course have the following:
- Completed the AWS Technical Essentials course
- Knowledge of machine learning and training language models
Course outline
- Lesson 1: Course Navigation
- Lesson 2: Introduction
- Lesson 3: Discovering Foundation Models
- Lesson 4: Deploying Foundation Models
- Lesson 5: Assessment
- Lesson 6: Conclusion
- Lesson 7: Contact Us
