An Amazon Bedrock tutorial for beginners
Generative AI is a growing technology that aims to improve productivity, reduce costs and can even help with decision-making. However, users interested in building these models need the know-how to get it done.
This introductory tutorial for Amazon Bedrock explores this rapidly evolving AWS service that helps developers build and integrate popular large language models (LLMs) and AI models into their agentic applications, all fully managed and using serverless capabilities.
What is Amazon Bedrock?
Amazon Bedrock creates an environment where software developers can easily connect to, query and integrate the output of LLMs from popular foundational model providers such as Anthropic, Meta and Luma AI Labs.
The Amazon Bedrock dashboard, integrated into the AWS Management console, provides a variety of tools to help AI architects evaluate which LLMS are best suited to their environments. It can also be a valuable tool for anyone interested in learning more about Generative AI.
Integrate AI with this Amazon Bedrock tutorial
In this Amazon Bedrock tutorial for beginners, you'll not only learn about what Amazon Bedrock is, but also how to do the following:
- Request access to foundational models.
- Compare LLMs like DeepSeek and Llama.
- Take advantage of model tokenization.
- Check for watermarks to see if content is AI-generated.
- Integrate foundational models with model context protocol APIs.
- Create videos and images inside the AWS console.
- Add guardrails to your LLM interactions.
- Write Lambda functions in Python and use the Bedrock API.
Those interested in the creation of RAG-based applications, chatbots, virtual assistants or any other type of application that can use functional models in a serverless manner should explore the managed services and features Amazon Bedrock has to offer.
Cameron McKenzie has been a Java EE software engineer for 20 years. His current specialties include Agile development; DevOps; Spring; and container-based technologies such as Docker, Swarm and Kubernetes.