Autoplay
Autocomplete
Previous Lesson
Complete and Continue
5 AWS Cloud Projects to Become a Cloud AI/ML Engineer
Introduction
0.1 Welcome
0.2 Getting Technical Support for these 5 Projects [Slack Community]
0.3 Course Introduction
0.4 Project Costs Breakdown
Project 1: Build a Serverless Smart Inbox with Real-Time Sentiment Analysis
1.1 Project Overview (0:31)
1.2 Create S3 Inbox for Incoming Messages
1.3 Create SQS Queues for Message Routing
1.4 Build the Lambda Function with Amazon Comprehend for Sentiment Analysis
1.5 Connect S3 Event to Lambda Trigger
1.6 Visualizing the Smart Inbox Results
1.7 Conclusion and Cleanup
Project 2: Intelligent FAQ Chatbot using Amazon Bedrock (RAG)
2.1 Project Overview (1:33)
2.2 Environment Setup and Prerequisites
2.3 Understanding RAG
2.4 Create Knowledge Base in Bedrock
2.5 Query and Test the Knowledge Base
2.6 Launching the Web Application (1:33)
2.7 Conclusion and Cleanup
Project 3: Predicting Customer Subscriptions with Amazon SageMaker
3.1 Project Overview
3.2 Environment Setup and Permissions
3.3 Data Preparation
3.4 Model Training
3.5 Model Deployment
3.6 Testing & Validation
3.7 Conclusion and Cleanup
Project 4: Image Emotion Detector using Hugging Face Vision Transformer
4.1 Project Overview (0:36)
4.2 Frontend Setup: Build the Upload Interface
4.3 Building the Emotion Detection Function using Hugging Face
4.4 Expose the Emotion Detector via API Gateway
4.5 Hosting the Frontend Testing (0:36)
4.6 Conclusion and Cleanup
Project 5: AI-Powered Cloud Learning Assistant using Gemini API
5.1 Project Overview (2:01)
5.2 Environment Setup and Permissions
5.3 Creating the Lambda Function and API Gateway
5.4 Adding DynamoDB Integration
5.5 Deploying the Web App using AWS Amplify
5.6 Conclusion and Cleanup
1.3 Create SQS Queues for Message Routing
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock