Week 10 Worklog

Week 10 Objectives:

  • Get familiar with and practice AI/ML services in the AWS ecosystem.
  • Understand the training, deployment and consumption lifecycle for Machine Learning models.
  • Hands-on with SageMaker, Rekognition, Comprehend, and Kendra.

Tasks to be carried out this week:

DayTaskStart DateCompletion DateReference Material
2- Overview of AI/ML on AWS
- Learn ML-supporting services: SageMaker, Rekognition, Comprehend, Kendra, Translate, Polly		10/11/202510/11/2025AWS Journey
3- Hands-on with Amazon SageMaker:
+ Create a Notebook Instance
+ Train a simple model (Linear Regression / Image Classification)
+ Deploy an endpoint and test predictions		11/11/202511/11/2025AWS Journey
4- Explore Amazon Rekognition
- Demo face & object recognition in images/videos
- Integrate Rekognition API into a small web app		12/11/202512/11/2025AWS Journey
5- Practice Amazon Comprehend (NLP)
- Try Amazon Kendra (contextual search)
- Compare strengths and limitations of each service		13/11/202513/11/2025AWS Journey
6- Week wrap-up:
+ ML development lifecycle on AWS
+ Real-world AI/ML applications
+ Write a summary report and next-step recommendations		14/11/202514/11/2025AWS Journey

Week 10 Achievements:

  • Understood the AWS AI/ML ecosystem and its core services.

  • Successfully trained and deployed a basic ML model on SageMaker.

  • Applied Rekognition, Comprehend, and Kendra to practical tasks.

  • Learned the end-to-end ML training → deploy → integrate workflow on AWS.