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The manual explains how to set up a high-performing machine-learning infrastructure on AWS for large-scale workloads, covering EC2, S3, and SageMaker configuration, encryption and access control, and monitoring with CloudWatch and alarms. 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other authors’ written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Rahul Poppad  \nDate: 23-04-2025  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is.\u003Cbr>not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nConfiguration Manual  \nRahul Poppad  \nStudent ID-23235535  \n1 Introduction  \nThis configuration manual serves as a guide to set up a high-performing ML infrastructure on AWS tailored to large-scale machine learning workloads. It walks users through configuring services such as Amazon EC2, Amazon S3, and Amazon SageMaker for scalable, cost-efficient ML applications. The report also covers monitoring using Amazon CloudWatch, ensuring a robust, flexible environment capable of supporting research, production, or development.  \n2. System Requirements  \nThe system requires AWS services including S3 for storage with versioning and CRR, CloudFront for content delivery, Route 53 for DNS failover, and AWS Backup for automated snapshots. Additional tools include IAM for access control, KMS for encryption, WAF and Shield for security, and CloudWatch for monitoring and alerts.  \n2.1 Hardware Requirements  \n• Access to EC2 instances with GPU support (e.g., p3, g4dn.xlarge, 2xlarge)  \n• Adequate disk space for model storage and processing.  \n• Network configuration allows public IP access if required.  \n2.2. Software Requirements  \n• Active AWS account with configured billing and access controls.  \n• Python installed with libraries such as boto3 .  \n• AWS CLI for command-line automation.  \n• I AM roles configured for EC2, S3, and Sage Maker access.  \n3. Installation  \n• Setting Up Amazon EC2  \n• Go to AWS Management Console → EC2 Dashboard.  \n• Launch a new instance using a Deep Learning AMI or custom Ubuntu AMI.  \n• Choose instance type (e.g., p3, g4dn.xlarge) .  \n• Configure networking, IAM roles, and storage.  \n• Enable public IP if needed and set up security groups (e.g., SSH on port 22) .  \n• Launch instance; use Spot Instances for cost-saving.  \n• Configure Auto Scaling and monitor performance with CloudWatch.  \n4. Configuration  \n. Amazon S3 for Data Storage  \n• Create bucket via S3 Dashboard; select region near EC2 instance.  \n• Enable versioning, logging; configure IAM permissions.  \n• Enable server-side encryption and optionally S3 Transfer Acceleration.  \nAmazon S","cbCaimWSNzlV4w41","https://ap.wps.com/l/cbCaimWSNzlV4w41","pdf",255788,"English","# 1 Introduction\n# 2 System Requirements\n## 2.1 Hardware Requirements\n## 2.2 Software Requirements\n# 3 Installation\n# 4 Configuration\n# 5 Usage\n# 6.Troubleshooting\n# 7.References","[{\"question\":\"Which AWS services are configured to support large-scale machine learning workloads?\",\"answer\":\"The manual guides configuration of Amazon EC2 for compute, Amazon S3 for storage, Amazon SageMaker for training and deployment, and Amazon CloudWatch for monitoring and logging.\"},{\"question\":\"What system requirements and access controls are needed before installation?\",\"answer\":\"It requires an active AWS account, IAM roles for EC2/S3/SageMaker access, Python libraries (e.g., boto3), AWS CLI, and supporting security components like KMS, WAF/Shield, and backup capabilities.\"},{\"question\":\"How does the manual recommend handling cost and performance for ML workloads?\",\"answer\":\"It suggests using GPU-enabled EC2 instances with Spot Instances for cost savings and enabling Auto Scaling while monitoring metrics and alarms via CloudWatch to maintain performance.\"}]","OPTIMZING CLOUD INFRASTRUCTURE TO SUPPORT LARGE-SCALE MACHINE LEARNING WORKLOADS - Configuration Manual | PDF",1789641537,13]