• Cloud computing has transformed the way organizations deploy, manage, and scale applications, making it a core technology in todays data-driven world. Microsoft Azure, one of the leading cloud platforms, provides a comprehensive set of tools and services for building, deploying, and managing applications. For students, utilizing Azure in their final year projects provides an opportunity to work with cutting-edge technologies in cloud computing, machine learning, big data, IoT, and more.
  
  The integration of Azures powerful cloud services with modern programming languages like Python, Java, and C# allows students to work with real-world scenarios, preparing them for careers in cloud computing, DevOps, data science, and more.Microsoft Azure plays a critical role in helping students develop cloud-based solutions for their final year projects. Its comprehensive set of services and tools enables students to build scalable, secure, and innovative applications across various domains. By leveraging Azure, students gain valuable industry experience, learn cloud-native development techniques, and acquire the skills necessary to thrive in todays cloud-focused IT landscape.

Software Tools and Technologies

• • Operating System: Ubuntu 20.04 LTS 64bit / Windows 10
• • Development Tools: Netbeans IDE 22 / Azure Data Studio 1.49.1 / Azure SDK 8.0.0,Azure CLI 2.64.0 / Visual Studio Code 2022
• • Language Version: Apache NetBeans IDE 22 / Python 3.11.7 / .NET 9 / Go 1.23
• Azure Open Source Tools for Cloud Computing

List Of Final Year Projects in Azure

• • Optimizing Azure Cloud Resource Allocation and Cost Forecasting with Time Series Analysis.
• • A Comparative Analysis of Automated ML (AutoML) vs. Custom Deep Learning Models for Medical Image Classification on Azure.
• • A Hybrid Deep Learning Model (CNN-LSTM) for Epileptic Seizure Prediction from EEG Data on Azure Kubernetes Service (AKS).
• • AI-Driven Defect Detection in Manufacturing: A Semantic Segmentation Approach using Azure ML and Azure Stack Edge.
• • A Benchmarking Framework for Latency and Bandwidth in IoT Architectures: Comparing Cloud-Centric vs. Edge-Centric Models on Azure.
• • Predictive Maintenance for Industrial IoT using Azure Machine Learning and IoT Hub
• • Federated Learning for Healthcare on Azure: Training MRI Segmentation Models Across Distributed Hospitals without Sharing Raw Data
• • Log Analytics at Petabyte Scale: Correlating Security Events Across Hybrid Cloud (Azure Arc) using Azure Data Explorer (Kusto)
• • Optimizing Manufacturing Energy Consumption with Real-Time IoT Sensor Analytics on Azure Digital Twins.
• • Predictive ETA and Route Optimization for Fleets using Azure IoT, Maps, and Machine Learning.
• • Real-Time Healthcare Monitoring with Predictive Analytics.
• • SLA-Aware Resource Allocation in Azure Kubernetes Service (AKS).
• • Blockchain-Enabled Secure Data Sharing on Azure.
• • Energy-Efficient Workflow Scheduling in Hybrid Azure Cloud.
• • Federated Learning with Privacy Preservation on Azure Edge.
• • Benchmarking Big Data Processing on Azure: A Performance and Cost Analysis of HDInsight (Spark) vs. Databricks for ETL Workloads.
• • Building a Real-Time Anomaly Detection System for IoT with Azure Event Hubs and Azure Stream Analytics.
• • A Comparative Study of Stream Processing Engines: Azure Stream Analytics vs. Apache Flink on HDInsight.
• • Architecting a Modern Data Lakehouse: Performance Testing Delta Lake on Databricks vs. Azure Synapse Analytics.
• • Optimizing Data Ingestion Patterns: Comparing Kafka on HDInsight with Azure Event Hubs for High-Throughput Scenarios.