Amazon Bedrock Customization, Optimization & Automation
Last updated 11/2025
Duration: 1h 23m | .MP4 1280x720 30fps(r) | AAC, 44100Hz, 2ch | 1.16 GB
Genre: eLearning | Language: English
Artificial Intelligence
What you'll learn
- Compare distillation with other optimization methods.
- Understand the difference between fine-tuning and continued pretraining.
- Explain model customization techniques in Amazon Bedrock.
- Evaluate model outputs using built-in tools.
Requirements
- AWS experience
Description
We can't wait to help you grow your generative AI expertise with this course focusing on customizing, optimizing, and automating AI solutions using Amazon Bedrock. This course is designed for developers who want to fine-tune their AI applications for peak performance and efficiency.
You'll begin by exploring model customization techniques, including fine-tuning and continued pre-training. Learn how to adapt foundation models to your specific use cases, enhancing their performance on domain-specific tasks.
The course then dives into advanced optimization strategies. You'll work with Bedrock Evaluation Jobs to assess and compare model performance, implement prompt caching for improved response times, and utilize prompt routing for efficient model selection.
In the automation section, you'll discover how to streamline AI workflows using Bedrock Data Automation. This tool will enable you to process and transform large datasets.
Throughout the course, you'll work in hands-on labs and real-world scenarios, applying these advanced techniques to solve complex AI challenges. By the conclusion of the course, you'll be designing, implementing, and maintaining AI solutions, stretching the limits of what's possible with generative AI on AWS.
Please note: The hands-on exercises are optional and require access to your own AWS account. Completing these activities may result in minimal usage charges.
Who this course is for:
- This course is designed for experienced AI developers who have already implemented generative AI solutions using Amazon Bedrock.
More Info
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