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!
Ultimate AWS Bedrock Guide: Build and Scale Generative AI Apps
3h 28m 1s
English
Paid
Ultimate AWS Bedrock Guide: Build and Scale Generative AI Apps is a 37-lesson 3 hours 28 minutes self-paced course by Zero To Mastery. Learn the cutting-edge world of AWS Bedrock and gain skills to create AI apps.
Course facts
- Lessons
- 37
- Duration
- 3 hours 28 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- Zero To Mastery
- Price
- Premium
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!
Additional
Who teaches Ultimate AWS Bedrock Guide: Build and Scale Generative AI Apps? Zero To Mastery
Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.
The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.
The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.
What lessons are included in Ultimate AWS Bedrock Guide: Build and Scale Generative AI Apps?
0:00
/ #1: Introduction
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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Frequently asked questions
What prerequisites are needed before enrolling in this course?
Before enrolling, students should have a basic understanding of cloud computing and familiarity with AWS services. Some experience with programming, particularly in Python, will be beneficial as the course involves coding Lambda functions and integrating them with AWS Bedrock.
What projects will I build during the course?
The course involves building and deploying three AI projects using AWS Bedrock. Students will work with foundation models from providers like Meta, Anthropic, Cohere, and Mistral. Projects include setting up a code generation project, creating a content summarization tool, and invoking a Stable Diffusion model for image generation.
Who is the target audience for this course?
This course is designed for developers and IT professionals interested in leveraging AWS Bedrock to build generative AI applications. It is also suitable for those seeking to expand their knowledge of AI models and cloud-based deployment.
How does the depth of this course compare to other AWS courses?
This course specifically focuses on AWS Bedrock and its application in building generative AI apps. It offers detailed lessons on setting up AI projects, deploying models, and integrating with AWS services, providing a focused approach compared to general AWS introductory courses.
What specific tooling or platforms are covered in the course?
The course covers AWS Bedrock extensively, including tools like AWS Lambda, API Gateway, and Boto3. It also involves working with foundation models from various providers, making use of AWS services for deployment and execution of AI applications.
What topics are not covered in this course?
The course does not cover basic AWS account setup or general cloud computing concepts in detail. It assumes prior knowledge of AWS and programming, focusing specifically on AWS Bedrock and its application in generative AI.
How much time should I expect to commit to this course?
The course consists of 37 lessons with hands-on projects. While the runtime is not specified, students should allocate sufficient time for both theoretical lessons and practical project work, potentially spanning several weeks depending on individual pace and prior experience.