• Amazon Web Services (AWS) has become one of the most popular and widely used cloud platforms, providing a comprehensive suite of cloud services that allow businesses and developers to build, deploy, and scale applications in a flexible and cost-efficient manner. AWS is used across various industries, including healthcare, finance, e-commerce, and entertainment, due to its scalability, security, and versatility. For students undertaking final year projects, AWS offers a valuable opportunity to work with real-world cloud technologies and develop innovative, scalable solutions.
  
  AWS provides a wide range of services that are ideal for building solutions across different domains, such as machine learning, data analytics, artificial intelligence (AI), Internet of Things (IoT), and web application development. Using AWS, students can experience cloud-based development firsthand, deploying applications in real-time environments, while benefiting from the platforms cost-effective pricing models and scalability.

Software Tools and Technologies

• • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10
• • Development Tools: AWS SDK 3.66.4.0 / AWS CLI 2.17.49
• • Language Version: JAVA SDK 21.0.2 / Python 3.11.7 / Go 1.23.2 / RubyMine 2024.1 / RStudio 1.2 / Kotlin 2.0.20
• Amazon Web Services(AWS) Open Source Tools for Cloud Computing

List Of Final Year Projects in AWS

• • Machine Learning-Based Energy Optimization in Smart Homes using AWS IoT and SageMaker Edge.
• • Medical Image Classification Using Deep CNNs on Amazon SageMaker with HIPAA Compliance.
• • Cost-Efficient Model Tuning with Hyperparameter Optimization using SageMaker Experiments.
• • Cloud-to-Edge Model Deployment for Low-Latency IoT Applications Using AWS IoT Greengrass V2.
• • Energy-Aware Resource Scheduling for IoT Workloads in Fog-Cloud Architecture with AWS IoT
• • Privacy-Aware Federated Learning Framework for Edge-Based IoT Devices using AWS Services
• • Resource-Aware Big Data Workflow Orchestration and Optimization Using AWS Serverless Services
• • Dynamic Auto-Scaling for Big Data Pipelines Using AWS Monitoring and Analytics Services
• • Large-Scale Image Classification Using Distributed ML on AWS EMR and SageMaker.
• • Edge-AI Powered Predictive Maintenance for Industrial IoT Using AWS Cloud Services.
• • Cost-Performance Optimization of Batch Analytics: Comparing Hadoop MapReduce on EMR vs. AWS Glue for Large-Scale ETL Pipelines.
• • Benchmarking Real-time Processing Engines: A Latency and Throughput Analysis of Apache Flink vs. Spark Structured Streaming on Amazon EMR.
• • Orchestrating a Multi-Engine Big Data Pipeline on AWS: Integrating Kafka, Spark, and Cassandra using Step Functions.
• • From Batch to Real-Time: Migrating a Traditional Hadoop Hive Warehouse to a Kafka-Centric Streaming Platform on AWS.
• • The Serverless Data Lake: Ingesting and Transforming Data with Kinesis, Lambda, and AWS Glue.
• • QoS-Aware Serverless Workflow Scheduling in AWS Lambda.
• • Energy-Efficient Data Lake on AWS.
• • Federated Learning on AWS IoT for Edge Devices.
• • AI-Driven Auto-Scaling for Cloud-Native Applications.
• • Secure Data Sharing with Attribute-Based Encryption in AWS.