Why this course?

Description

This course provides a comprehensive introduction to AWS (Amazon Web Services), covering key concepts in cloud computing, web app deployment, serverless computing, machine learning, networking, security, and more. The modules include practical hands-on experiences with AWS services, teaching you how to leverage AWS for building cloud-based applications, deploying microservices, creating machine learning models, managing storage and databases, and integrating with various AWS tools.

Overview: (12-20 hours)

This course is designed to help learners understand and effectively utilize AWS in their cloud projects. By the end of the course, students will have practical knowledge and experience with AWS tools, allowing them to deploy, manage, and scale applications in the cloud efficiently. Topics range from the basic setup of an AWS account to advanced applications such as machine learning, AI services, and serverless architectures.

Projects:

1. Web App Deployment with Elastic Beanstalk – Deploying a web application using AWS Elastic Beanstalk for simplified scaling and management.
2. Image Resizer with AWS Lambda – Creating a serverless image resizing application using AWS Lambda.
3. SES, SNS Alerts with Lambda – Setting up email notifications and alerts using Amazon SES and SNS, with AWS Lambda for event-driven execution.
4. Networking - API Gateway Project with Lambda – Building an API using AWS API Gateway and Lambda for a serverless API management solution.
5. AWS AI and ML Integration – Using AWS Sagemaker and other AWS AI services for machine learning and model development.
6. Chatbot with AWS Lex – Developing an intelligent chatbot with AWS Lex.

Tools Used:

• AWS Management Console: Interface to manage AWS resources.
• AWS Elastic Beanstalk: Platform for deploying and managing web applications.
• AWS Lambda: Serverless computing service.
• AWS S3: Scalable storage service for backups and file storage.
• AWS RDS: Managed relational database service.
• Amazon QuickSight: Business intelligence service for analytics.
• AWS Glue: Managed ETL service.
• Amazon API Gateway: Tool to create and manage APIs.
• AWS Lex: Service for building conversational interfaces (chatbots).
• Amazon Sagemaker: Managed service for building, training, and deploying machine learning models.

Skills Required:

• Basic understanding of cloud computing.
• Familiarity with programming languages like Python (preferred).
• Basic knowledge of web application architecture.
• Knowledge of databases and APIs.
• Understanding of serverless architecture.
• Familiarity with AWS platform and services.
• Ability to work with AWS SDKs and command-line tools.
• Understanding of machine learning concepts (helpful for projects like AWS Sagemaker).

 

 

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Days and Topics:

Day 1 - Introduction to Cloud Computing and Account Creation on AWS

Day 2 - AWS Setup & Budgets

Day 3 - AWS

Day 4 - AWS

Day 5 - Web App Deployment with Elastic Beanstalk

Day 6 - Storage Service in AWS

Day 7 - Database in AWS

Day 8 - AWS Lambda Introduction

Day 9 - Image Resizer with Lambda

Day 10 - SES, SNS Alerts with Lambda

Day 11 - Microservices, Containers, Event Bridge

Day 12 - Amazon QuickSight

Day 13 - Glue ETL

Day 14 - AWS Security: Cognito, Secrets Manager, Inspector

Day 15 - Networking: Amazon API Gateway Project with Lambda

Day 16 - AWS Sagemaker, Machine Learning, Sagemaker Canvas

Day 17 - AWS AI Services

Day 18 - Chatbot with AWS Lex

Day 19 - AWS Developer Tools

Day 20 - AWS Amplify

Day 21 - Additional Internship Concepts

Day 22 - Additional Projects Internship User

Day 23 - Top 100 Interview Questions on AWS