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ZerotoMastery – AWS Bootcamp Build AI Apps with AWS Bedrock
ZerotoMastery – AWS Bootcamp Build AI Apps with AWS Bedrock
Released 6/2026
By Patrik Szepesi
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 135 Lessons ( 15h 17m ) | Size: 3.4 GB
The perfect course to take the next step in your AI learning and start building AI applications that scale. Learn the cutting-edge world of AWS Bedrock and gain AI skills by building and deploying six AI projects using foundation models from OpenAI, Anthropic, Google, Cohere, Mistral and more, all from a single API!
What you'll learn
Build and deploy Generative AI applications using AWS Bedrock
Summarize documents and extract key decisions with AI pipelines
Generate text, code, and images using foundation models
Improve model behaviour with fine-tuning and reinforcement fine-tuning
Evaluate and monitor LLM outputs for safety and quality
Deploy scalable, serverless AI systems on AWS
Build intelligent multi-agent AI systems
Create real-time voice AI agents using AWS Bedrock Nova Sonic
What Is AWS Bedrock?
AWS Bedrock is Amazon Web Services' fully managed platform for building Generative AI applications. It lets you work with powerful foundation models while abstracting away infrastructure complexity.
This course will take you from zero to building production-ready AI applications...even if you're completely new to Generative AI!
Projects will integrate AWS Bedrock with API Gateway, AWS Lambda, S3, AgentCore, Postman, and other essential AWS services.
Why This AWS Bedrock Course?
Well, because it's the best, most up-to-date, and practical AWS Bedrock course online that lets you get hands-on by building six AWS Bedrock projects so that you can actually use your skills in the real-world.
But of course we're biased. So here's some more details about
What you'll learn
in this AWS Bedrock course so that you can make up your own mind1. AWS Bedrock & Generative AI Foundations Understand what AWS Bedrock is, how it fits into the Generative AI ecosystem, and how modern AI applications are built using managed foundation models — explained in a beginner-friendly way.
2. Large Language Models (LLMs) Learn how LLMs work, what they excel at, and where their limitations lie. Explore real-world use cases including document understanding, chat systems, and code generation using Bedrock-hosted models.
3. Fine-Tuning & Reinforcement Fine-Tuning (RFT) Go beyond basic prompting. Learn how models can be improved using fine-tuning and RFT with human feedback, when and why to use these techniques, and how they fit into modern AI workflows.
4. Prompt Management & Optimization Design, manage, version, and optimize prompts for consistent, reliable results. Learn why prompt management becomes critical as applications scale and evolve.
5. Intelligent Model Routing Discover how to route requests intelligently between different models based on task type, cost, or quality — choosing the right model at the right time.
6. Batch Inference & Data Automation Process large volumes of data efficiently using batch inference, and automate document and data workflows using AWS Bedrock for scalable AI processing.
7. Image Generation with Diffusion Models Explore image generation concepts using diffusion models — including how to generate, control, and deploy image-based AI features at scale.
8. AI Safety, Evaluation & Watermark Detection Evaluate your LLMs for quality, accuracy, and toxicity. Understand AI safety concepts including watermark detection and responsible AI usage.
9. Serverless Deployment & Scaling Deploy Generative AI applications using a fully serverless stack — AWS Lambda, API Gateway, S3, and Bedrock — and scale AI applications without managing any servers.
10. AgentCore: From Prototype to Production Take your agents beyond local development. Learn what Amazon Bedrock AgentCore is, deploy agents on AgentCore Runtime, and monitor every tool call and decision with built-in observability — no servers to manage, production-ready from day one.
11. Agent Memory — Short-Term & Long-Term Give your agents the ability to remember. Implement short-term memory using get_last_k_turns so agents track conversation context within a session. Then add long-term memory with extraction strategies that automatically capture semantic facts, user preferences, and session summaries — so your agents personalize every interaction, even across sessions.
12. Guardrails & Responsible AI Learn how to use Amazon Bedrock Guardrails to add safety controls, filter harmful content, reduce risk, and enforce responsible AI behavior across your applications.
13. Model Comparison Compare different foundation models based on quality, speed, cost, capabilities, and use case fit so you can choose the right model for each task.
14. AWS Model Usage Quotas Understand AWS Bedrock model usage quotas, request limits, throttling, and how quotas affect real-world production applications.
15. Inference Profiles Learn how inference profiles work in Amazon Bedrock and how they help with scalable, reliable, and cross-region model invocation.
16. Marketplace Models on AWS Bedrock Explore Amazon Bedrock Marketplace Models and learn how to access additional foundation models beyond the default Bedrock catalog.
17. Advanced Bedrock Features Go beyond basic prompting and deployment by working with more production-focused AWS Bedrock capabilities used in real AI systems.
Homepage
https://zerotomastery.io/courses/learn-aws-bedrock/
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