Week 6 Worklog

22/05/2026

Work Completed

  • Reviewed knowledge from previous weeks on storage, networking, and monitoring.
  • Prepared topics and questions ahead of the next AWS event.
  • Read additional AWS Blog posts related to AI services and architecture use cases.

Results Achieved

  • Reinforced knowledge gained in previous weeks.
  • Felt better prepared before attending the AWS event.
  • Started paying greater attention to AI/ML managed services on AWS.

23/05/2026

Work Completed

  • Attended an AWS event at Bitexco.
  • Listened to sessions on GenAI, QuickSight, CloudFront, multi-agent systems, and real-world AWS use cases.
  • Noted key content on how to present a cloud solution, how businesses apply AI, and how to combine multiple AWS services in a single architecture.

Results Achieved

  • Gained practical knowledge on modern AWS topics including GenAI, analytics, and content delivery.
  • Gained a deeper understanding of how AWS services are used to solve real-world problems.
  • Collected content for use in blog writing, internship reporting, and guiding further study.

24/05/2026

Work Completed

  • Summarized notes from the event on 23/05.
  • Organized the content into groups: GenAI, analytics, CloudFront, system architecture, and professional takeaways.
  • Read additional documentation related to the topics discussed at the event.

Results Achieved

  • Produced a clearer and more structured set of notes after the event.
  • Understood that AWS services can be combined to build a wide variety of solutions, from analytics dashboards to AI-powered applications.
  • Gained a new perspective on learning AWS through practical, use-case-driven approaches.

25/05/2026

Work Completed

  • Studied AI/ML managed services available on AWS.
  • Explored the concept of managed services and the benefits of using existing services instead of building everything from scratch.
  • Read examples covering speech-to-text, translation, text analytics, and GenAI on AWS.

Results Achieved

  • Understood that managed services reduce operational and deployment overhead.
  • Learned that AWS provides a wide range of AI services that can be integrated directly into applications.
  • Built foundational knowledge on how cloud supports AI use cases without requiring custom model training.

26/05/2026

Work Completed

  • Studied core cloud architecture design principles.
  • Read about concepts such as scalability, reliability, security, performance efficiency, and cost optimization.
  • Noted the factors to consider when designing a system on cloud.

Results Achieved

  • Understood that cloud architecture design requires balancing multiple factors, not just ensuring the system runs.
  • Learned basic concepts related to reliability, performance, security, and cost management.
  • Built a foundation to continue studying the AWS Well-Architected Framework.

27/05/2026

Work Completed

  • Continued studying AWS architecture and cloud solution design on Coursera.
  • Reviewed the differences between compute, storage, database, networking, security, and monitoring within a system.
  • Noted how these service groups are typically combined in a web application.

Results Achieved

  • Gained a clearer view of a cloud system as multiple layers: frontend, backend, storage, network, security, and monitoring.
  • Developed the ability to classify services by function rather than studying each in isolation.
  • Felt better prepared for the phase of applying knowledge to a real project in the coming weeks.

28/05/2026

Work Completed

  • Summarized the AWS self-study journey from Week 1 through Week 6.
  • Reviewed all topics covered: AWS fundamentals, IAM, EC2, Lambda, S3, CloudFront, VPC, CloudWatch, and AI managed services.
  • Prepared to transition into the next phase: selecting a project topic and applying AWS knowledge to a real implementation.

Results Achieved

  • Completed the foundational AWS self-study phase across the first six weeks.
  • Gained a broader view of the AWS ecosystem and how its services interconnect.
  • Ready to move into real-world project development starting from Week 7.

Weekly Summary

This week, I continued expanding my AWS knowledge through Coursera, AWS Blog posts, and a real-world AWS event. I studied AI/ML managed services, explored real-world use cases, and learned core cloud architecture principles such as scalability, reliability, security, performance efficiency, and cost optimization. After this week, I have a clearer understanding that AWS is an ecosystem of interconnected services that can be combined to build complete solutions — and the six-week self-study phase has built the foundation needed to apply this knowledge to a real project from Week 7 onward.