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1.
Week 1-12 Worklog
1.1.
Week 1 Worklog
1.2.
Week 2 Worklog
1.3.
Week 3 Worklog
1.4.
Week 4 Worklog
1.5.
Week 5 Worklog
1.6.
Week 6 Worklog
1.7.
Week 7 Worklog
1.8.
Week 8 Worklog
1.9.
Week 9 Worklog
1.10.
Week 10 Worklog
1.11.
Week 11 Worklog
1.12.
Week 12 Worklog
2.
Project Proposal
3.
Technical Blog Posts
3.1.
Blog 1: From Real-Time AI Assistant to Realistic AWS MVP
3.2.
Blog 2: Designing Serverless AI Workflows with Step Functions
3.3.
Blog 3: Security, Privacy, and Cost Boundaries for AI Conversation Data
4.
Events Participated
4.1.
Event 1: FCAJ AWS Bootcamp Orientation
4.2.
Event 2: AWS Study Group Technical Sharing
5.
Technical Workshop
5.1.
Workshop Overview
5.2.
Prerequisites
5.3.
Create Storage and Job Foundation
5.3.1.
Create S3 Bucket and DynamoDB Table
5.3.2.
Create Upload and Result API
5.4.
Build the AI Processing Workflow
5.4.1.
Prepare Sample Input and Prompt
5.4.2.
Add Amazon Transcribe for Audio Input
5.4.3.
Invoke Bedrock from Lambda
5.4.4.
Orchestrate and Validate the Full Workflow
5.5.
Security, Monitoring, Testing, and Cost Optimization
5.6.
Cleanup
6.
Self Evaluation
7.
Feedback and Sharing
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Events Participated
> Event 2: AWS Study Group Technical Sharing
Main Content
Lessons Learned / Contribution
Event 2: AWS Study Group Technical Sharing
Field
Detail
Event name
AWS Study Group Technical Sharing
Date/time
TODO: Add actual date and time
Location
TODO: Add online/offline location
Role
Participant
Main Content
Practical AWS service usage and architecture discussion.
Serverless design patterns for small projects.
IAM and security best practices.
Cost control and cleanup reminders for learning accounts.
Lessons Learned / Contribution
I learned to justify why each AWS service is used instead of adding services only to increase the count.
I applied the cost and security lessons to my Cognitive Communication Coach architecture.
I refined the project to avoid live conversation monitoring and focus on safer post-conversation analysis.