Amazon Bedrock Customization, Optimization & Automation

Grow 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.

• Learn model customization techniques, including fine-tuning foundation models with domain-specific data and implementing continued pre-training strategies to enhance model performance for specialized tasks
• Develop expertise in model evaluation and optimization using Bedrock Evaluation Jobs, learning to assess model performance, identify potential biases, and implement systematic testing approaches for quality assurance
• Implement performance optimization strategies including prompt caching and routing, enabling faster response times and more efficient resource utilization in your AI applications
• Design and implement automated AI workflows using Bedrock Data Automation, learning to process and transform large-scale datasets
• Apply advanced techniques for model efficiency, including model distillation and automated testing pipelines