
Explore AWS bedrock, a fully managed AI foundation models service that acts as an API wrapper to models from multiple providers, enabling text generation, embeddings, and text-to-vision tasks.
Develop a mental model of AWS Bedrock as a plug-and-play generative ai service that lets you switch models for text, image, and chat tasks, with auto scaling and retrieval augmentation.
Explore foundation models from major providers, leverage AWS Bedrock's serverless, fully managed platform, customize with fine-tuning and rag for knowledge bases, and integrate via APIs with your data lake stack.
AWS bedrock is like Netflix for AI, enabling on-demand streaming of foundation models. Choose, customize, or Rag, and feed documents to build knowledge base; deploy via API and AWS scaling.
Set up foundation models in AWS Bedrock by configuring the backdrop, granting model access, and creating an IAM user, while noting costs and onboarding steps.
Create a Bedrock IAM user, attach basic policies, avoid administrator access, then generate an access key and download the CSV for use with GitHub Codespaces.
Fork the repository and set up Codespaces for the AWS Bedrock project. Install dependencies with npm and TSX, then run the code to verify functionality in your region.
Explore hyperparameters for AWS bedrock payloads, focusing on temperature, topic, and tokens; learn how temperature shapes creativity, topic governs vocabulary, and tokens determine response length.
Learn to use AWS Bedrock with Amazon Titan Express v1 to generate text from prompts by setting up a Bedrock client in TypeScript and tuning temperature and top p.
Learn to work with Claude on AWS Bedrock by invoking the model with its ID, sending text prompts, and comparing outputs with Titan, while navigating model-specific APIs and token settings.
Feed images to AWS bedrock's image-to-text model and encode them in base64 for JSON requests. Learn about model IDs, payloads, and throttling for production.
Learn to generate images from text using AWS Bedrock Titan image generator, configure prompts and API requests, and save outputs to an output directory.
Accelerate Your AI Journey with Amazon Bedrock
This course is specifically tailored for busy professionals seeking a practical and efficient pathway to mastering AWS Bedrock. Whether you're an engineer, developer, or a technology leader, you'll quickly acquire the essential knowledge and skills to leverage AWS Bedrock's powerful AI capabilities in your projects.
What to Expect from This Course:
Deep Understanding of AWS Bedrock’s Capabilities: Gain clarity and practical insights into what AWS Bedrock offers, empowering you to harness its full potential to drive innovation.
Exploration of Different Foundation Models & Use Cases: Examine the strengths and applications of various foundation models available within AWS Bedrock, enabling you to select the right tools for your specific business or project needs.
Hands-on Work with Text and Images: Learn to build AI-driven applications that intelligently process both textual and visual data, enriching user experiences and automating critical workflows.
How This Course Is Structured:
Cut-to-the-Chase Approach: This course is designed to respect your time. We eliminate unnecessary theory and deliver concise, relevant content to ensure rapid and effective learning.
Hands-on Setup & Code Understanding: Follow streamlined yet thorough guidance for quick setup, allowing you to spend more time actively engaging with AWS Bedrock and less time troubleshooting.
Detailed Code Walkthroughs & Logical Insights: Dive deep into structured code examples, fully explained to ensure you grasp the logic behind every line. This approach equips you to independently extend and adapt the examples to your unique requirements.
Join this course today and quickly become proficient in AWS Bedrock, transforming your ability to build innovative AI-powered solutions efficiently.