Mid-sized Technology Firm

CLIENT OVERVIEW: A mid-sized, innovation-driven technology firm was running a suite of applications and ML workflows on an on-premises VMware environment. This infrastructure had evolved over time into a complex and costly ecosystem with aging hardware, manual processes, and fragmented pipelines. While reliable initially, it was no longer meeting the growing demands for agility, security, and scalability required to support their AI and microservices roadmap.

• 1) Rising Infrastructure Costs: Hardware refresh cycles and VMware licensing created a significant recurring expense. Lack of elasticity resulted in over-provisioned environments, increasing both CapEx and OpEx.
• 2) Operational Complexity: Manual provisioning and scaling of VMs slowed down development and testing cycles. Teams were spending too much time on routine infra tasks instead of innovation.
• 3) Security & Access Gaps: Open ports and fragmented access policies posed compliance risks. VPN and SSH access were inconsistently enforced across environments.
• 4) Fragmented ML Workflow: Data scientists had to wait for infra support to train, test, and deploy models. There was no standardized pipeline or orchestration for end-to-end model lifecycle.
• 5) Lack of Observability: No centralized monitoring stack. Debugging latency, uptime, or network issues required ad hoc, manual investigation.

• PHASE 1: DISCOVERY & ASSESSMENT: Conducted a thorough assessment of on-prem VMware workloads using dependency mapping tools. Identified critical vs. non-critical workloads, usage patterns, and resource consumption. Defined the cloud readiness of each workload and grouped them into migration waves.
• PHASE 2: PLANNING & STRATEGY: Designed a target-state AWS environment leveraging EC2, EKS, S3, IAM, CloudWatch, and VPCs. Designed landing zones with network segmentation for Dev, UAT, and Prod. Developed Terraform-based Infrastructure-as-Code templates to standardize deployments.
• PHASE 3: MIGRATION EXECUTION: Dockerized legacy applications and migrated them to AWS EKS. Lift-and-shifted essential VMs to EC2 for quick wins, then gradually re-architected them. Introduced shared storage for container workloads to reduce redundancy and improve scaling.
• SECURITY ENGINEERING ENHANCEMENTS: Hardened network layer by closing unused ports and removing public IPs where unnecessary. Enforced VPN-based SSH access with role-based IAM policies. Enabled multi-zone deployment and backup policies to support DR and availability targets. Introduced AWS GuardDuty and Config Rules for continuous security posture monitoring.

• OBSERVABILITY & MONITORING: Implemented a full-stack Grafana + Prometheus setup for performance and uptime tracking. Integrated CloudWatch logs and alarms with Slack channels for real-time alerts. Built custom dashboards for infrastructure KPIs and application SLOs.
• MLOPS PIPELINE IMPLEMENTATION: Built an end-to-end CI/CD pipeline using GitHub Actions, integrating data preprocessing, training, validation, and model promotion. Models were containerized and deployed via Helm charts to EKS with versioned rollouts. Enabled auto-scaling of GPU/CPU nodes based on ML training loads.

• Infra Cost: Migrated from High CapEx + VMware Licensing to Pay-as-you-go AWS, resulting in 35–40% savings.
• Deployment Time: Reduced from Days/weeks to Hours (80% faster).
• Model Lifecycle: Transformed from Manual to Automated pipeline (Standardized & scalable).
• Uptime Visibility: Upgraded from Ad hoc logs to Grafana dashboards (Real-time insights).
• Access Management: Hardened security with Role-based VPN + IAM.
• Infra Scalability: Future-ready elastic, auto-scaled infrastructure compared to previous vertical scaling only.

• Cost Optimization: VMware licensing eliminated, idle VM sprawl removed, and right-sized AWS workloads led to significant savings.
• Security Posture Strengthened: With IAM, VPN tunnels, hardened VPC configurations, and automated compliance checks, the infrastructure is now secure by design.
• Accelerated Innovation: Product and ML teams gained autonomy with ready-to-use environments and self-service deployment pipelines.
• Scalability & Elasticity: Containerized applications and ML models now scale automatically, supporting unpredictable workloads with ease.
• Modern Cloud-Native Foundation: From IaaS to Kubernetes to CI/CD-integrated MLOps, the organization now has a robust, cloud-native platform for future growth.

• AWS Services: EC2, EKS, S3, IAM, CloudWatch, VPC, GuardDuty
• DevOps: Docker, Helm, Terraform, GitHub Actions
• Monitoring: Grafana, Prometheus, CloudWatch Logs
• Security: AWS Config Rules, VPN tunneling, RBAC, GuardDuty
• MLOps: CI/CD Pipelines, Auto-scaling training jobs, EKS-native deployments