AWS Learning Syllabus
Table Of Contents:
- AWS Foundation
- AWS Global Infrastructure – Regions, Availability Zones, Edge Locations
- AWS Identity & Access Management (IAM) – Roles, Policies, Permissions
- Amazon EC2 – Virtual Machines, Auto Scaling, Load Balancing
- Amazon S3 – Data Storage, Security, Versioning, Lifecycle Policies
- AWS Lambda – Serverless Computing
- AWS CloudWatch & CloudTrail – Monitoring and Logging
- AWS Cost Management – Budgets, Pricing, Cost Optimization
- Data Storage & Databases
- Amazon S3 – Data lakes, Storage Classes, Permissions
- AWS Glue – ETL (Extract, Transform, Load) for data pipelines
- Amazon RDS – SQL databases (PostgreSQL, MySQL)
- Amazon DynamoDB – NoSQL database for scalable applications
- Amazon Redshift – Data warehousing for big data analytics
- Amazon Athena – Querying S3 data with SQL
- Big Data Processing on AWS
- Amazon EMR – Running Spark and Hadoop workloads
- AWS Kinesis – Real-time data streaming
- AWS Data Pipeline – Automating data workflows
- AWS Lake Formation – Creating secure data lakes
- Machine Learning on AWS
- Amazon SageMaker – Training, tuning, and deploying models
- SageMaker Studio – Jupyter-based development environment
- SageMaker Feature Store – Managing ML features
- SageMaker Autopilot – Automated ML model training
- SageMaker Ground Truth – Labeling datasets for ML
- Model Deployment & MLOps
- SageMaker Endpoints – Deploying models as APIs
- AWS Lambda for ML – Serverless inference
- Docker & Amazon ECR – Containerizing ML models
- Amazon API Gateway – Serving ML models as APIs
- AWS Step Functions – Automating ML workflows
- SageMaker Pipelines – CI/CD for ML models
- AWS CodePipeline – CI/CD for model deployment
- Security & Cost Optimization
- AWS IAM Best Practices – Managing access securely
- VPC (Virtual Private Cloud) – Securing ML models
- AWS PrivateLink – Secure API access
- AWS Cost Explorer & Budgets – Cost tracking and savings
- Final Capstone Project (Objective: Build an end-to-end ML pipeline on AWS.)
- Step 1: Store raw data in S3
- Step 2: Clean data using AWS Glue
- Step 3: Train an ML model using SageMaker
- Step 4: Deploy the model using SageMaker Endpoints / API Gateway
- Step 5: Monitor and optimize the model with CloudWatch & Step Functions