Week 8 Worklog

Week 8 Objectives:

  • Understand core AI, Machine Learning, Deep Learning, and Generative AI concepts.
  • Learn how to use managed Generative AI models with Amazon Bedrock.
  • Understand the Machine Learning lifecycle with Amazon SageMaker AI.
  • Explore managed AI services for image, document, and text analysis.
  • Compare AWS AI/ML services and select suitable services for different use cases.

Tasks to be carried out this week:

DayTaskStart DateCompletion DateReference Material
1Study AI/ML Concepts on AWS
- Learn Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.
- Understand datasets, training, inference, models, and endpoints.
- Compare managed AI services with an ML platform.
- Review common AWS AI/ML use cases.		08/06/202608/06/2026AWS Machine Learning
AWS AI Services
2Study Amazon Bedrock
- Learn foundation models, model providers, inference, prompts, and model parameters.
- Understand Amazon Bedrock Agents and Knowledge Bases.
- Review security, IAM permissions, and responsible use of Generative AI.
- Compare model selection based on quality, latency, and cost.		09/06/202609/06/2026Amazon Bedrock
What is Amazon Bedrock?
3Study Amazon SageMaker AI
- Learn the Machine Learning lifecycle from data preparation to model deployment.
- Understand datasets, training jobs, models, endpoints, and inference.
- Review built-in algorithms and supported ML frameworks.
- Compare Amazon SageMaker AI with Amazon Bedrock.		10/06/202610/06/2026Amazon SageMaker AI
What is Amazon SageMaker AI?
4Study AWS Managed AI Services
- Learn image and video analysis with Amazon Rekognition.
- Learn text, forms, and table extraction with Amazon Textract.
- Learn natural language and text analysis with Amazon Comprehend.
- Compare API-based AI services and custom ML model development.		11/06/202611/06/2026Amazon Rekognition
Amazon Textract
Amazon Comprehend
5Compare AWS AI/ML Services and Review Architecture Choices
- Compare Amazon Bedrock, Amazon SageMaker AI, Rekognition, Textract, and Comprehend.
- Identify suitable services for Generative AI, image analysis, document processing, NLP, and custom ML models.
- Review security, privacy, latency, scalability, and cost considerations.
- Document service selection criteria and AI/ML architecture decisions.
- Review responsible AI considerations and model limitations.		12/06/202612/06/2026Amazon Bedrock or Amazon SageMaker AI
AWS Responsible AI
6Improve the Group’s AWS Architecture Diagram
- Learn the basic draw.io tools and AWS architecture icons.
- Review the group’s existing diagram and identify incorrect components and connections.
- Edit and improve the diagram to make the architecture and data flow clearer.
- Prepare the updated diagram for technical documentation.		13/06/202613/06/2026draw.io
AWS Architecture Icons

Week 8 Achievements:

  • Understood core AI, Machine Learning, Deep Learning, and Generative AI concepts.
  • Understood the role of Amazon Bedrock in building Generative AI applications with foundation models.
  • Understood the Machine Learning lifecycle and deployment concepts of Amazon SageMaker AI.
  • Distinguished the use cases of Amazon Rekognition, Amazon Textract, and Amazon Comprehend.
  • Compared AWS AI/ML services based on use case, model customization, security, latency, scalability, and cost.
  • Documented responsible AI considerations and limitations when selecting AI/ML services.
  • Edited and improved the group’s AWS architecture diagram using draw.io.