Ultimate AWS Bedrock Guide: Build and Scale Generative AI Apps
Course description
Learn the cutting-edge world of AWS Bedrock and gain skills to create AI apps. You'll build and deploy three AI projects using foundation models from Meta, Anthropic, Cohere, Mistral and more, all from a single API!
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This course is made for anyone eager to take dive deep into building AI-driven applications using the latest technologies. By the end of this course you'll have a comprehensive understanding of AWS Bedrock and Generative AI, including Retrieval Augmented Generation (RAG), fine-tuning, vector databases, word embeddings, serverless architecture, training and evaluation of LLMs with your own custom data, and more!
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All Course Lessons (37)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 01:01 | |
| 2 | What We're Building | 04:44 | |
| 3 | Setting Up our AWS Account | 05:25 | |
| 4 | Login to IAM User Account | 04:24 | |
| 5 | What is AWS Bedrock? | 09:06 | |
| 6 | Introduction to Foundational Models | 04:33 | |
| 7 | Foundational Model Providers | 11:03 | |
| 8 | Bedrock Playground and Pricing | 14:03 | |
| 9 | Inspecting Model Inference Configurations for LLMs and Image Generation Models | 10:36 | |
| 10 | Training Foundation Models with your Custom Data | 07:34 | |
| 11 | Setting Up the Code Generation Project | 01:06 | |
| 12 | Coding our Lambda Function and Integrating with AWS Bedrock | 17:52 | |
| 13 | Setting up API Gateway and our Serverless Stack | 05:35 | |
| 14 | Testing our Live Endpoint | 05:58 | |
| 15 | Creating our Boto3 Lambda Layer | 02:24 | |
| 16 | Attaching our Lambda Layer to our Function | 02:25 | |
| 17 | Testing our Bedrock Model | 06:29 | |
| 18 | Verifying Final Output of Bedrock | 02:25 | |
| 19 | Setting up our Lambda Function with Bedrock for Content Summarization | 08:44 | |
| 20 | Finishing our Lambda Function for Meeting Summarisation | 14:11 | |
| 21 | Creating new API Gateway Endpoint for this Lambda Function | 02:16 | |
| 22 | Invoking our Serverless Meeting Notes Summarisation Endpoint | 06:31 | |
| 23 | Analyzing the Final Results | 01:40 | |
| 24 | Project Introduction | 10:58 | |
| 25 | Setting up API Gateway Route (Serverless) for New Generative AI Model Invocation | 01:07 | |
| 26 | Invoking our Stable Diffusion model for Image Generation | 03:49 | |
| 27 | Analysing our Final Output | 00:47 | |
| 28 | Set up Evaluation Job for Anthropic's Claude model | 04:38 | |
| 29 | Evaluating our Results | 05:26 | |
| 30 | Introduction to AWS Bedrock Knowledge Base | 02:58 | |
| 31 | Retrieval Augmented Retrieval (RAG) Overview | 06:41 | |
| 32 | Setting Up Our Own Knowledge Base - Part 1 | 08:09 | |
| 33 | Setting Up Our Own Knowledge Base - Part 2 | 00:51 | |
| 34 | Testing our Bedrock Knowledge Base with Antropic's Claude Model | 07:21 | |
| 35 | Clean Up Resources | 01:11 | |
| 36 | API Resources | 00:43 | |
| 37 | A Little Cleanup and Congratulations! | 03:17 |
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