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Network Systems and Administration / Cloud Computing / DevOps
2,500 words
Automated Container Deployment and Administration in the Cloud
This practical Network Systems and Administration assessment requires students to design and implement an automated cloud deployment workflow using modern DevOps and infrastructure automation technologies. The overall objective is to deploy a cloud-hosted server running a Docker container while integrating at least two automation tools such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub Actions or GitHub. Students may use a cloud platform such as AWS, Microsoft Azure or Google Cloud Platform. B9IS121 Network Systems and Adm… The first stage focuses on cloud infrastructure provisioning. Students automate the creation of a server instance, such as an AWS EC2 instance or Azure virtual machine, and configure supporting infrastructure including networking and security groups. Infrastructure must be provisioned through tools such as Terraform, GitHub Actions or AWS CloudFormation. The required outputs include reusable infrastructure scripts or templates and an architecture diagram showing the deployed cloud resources. B9IS121 Network Systems and Adm… The second stage addresses configuration management. Students use Ansible or an equivalent automation platform to configure the provisioned server, install Docker and ensure Docker starts automatically when the server boots. Deliverables include Ansible playbooks or equivalent configuration scripts together with a README file explaining the automation workflow. B9IS121 Network Systems and Adm… The third stage requires students to containerise and deploy a web application. A Dockerfile must be created for either an existing or newly developed sample application, and deployment of the resulting container must be automated on the provisioned server. The required artefacts include the Dockerfile, deployment scripts and application repository. B9IS121 Network Systems and Adm… Students must also create a CI/CD pipeline using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever new code is pushed to a version-control repository such as GitHub. Students must provide the relevant pipeline configuration, normally in YAML, and demonstrate the pipeline operating successfully. B9IS121 Network Systems and Adm… Documentation is an important part of the assessment. Each student produces an independent PDF report explaining every stage of the automation process, challenges encountered and how those challenges were resolved. A 10-minute walkthrough video must demonstrate the complete end-to-end deployment, and the report must contain a detailed architecture diagram. B9IS121 Network Systems and Adm… The technical report should include a title page, summary, introduction, main technical content, conclusion, references and appropriate appendices. Students are expected to justify the tools selected and critically discuss alternative solutions, advantages and limitations. The report is expected to contain approximately 2,000–2,500 words and should use clear headings, diagrams or flowcharts where relevant, and academic or professional sources such as journal articles, whitepapers, DevOps documentation and industry reports. B9IS121 Network Systems and Adm… B9IS121 Network Systems and Adm… The marking scheme allocates 40% to the demonstration, 50% to report quality and 10% to technical artefacts. Higher-performing submissions are expected to show a working deployment, clear justification of technical actions, strong understanding of automation tools, professional report structure, consistent referencing and reusable scripts that reproduce the same deployment results. B9IS121 Network Systems and Adm… The final submission includes a comprehensive PDF report of approximately 5–8 pages, a detailed README, and links to the relevant scripts, configurations and deployment evidence. B9IS121 Network Systems and Adm…
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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