Asad Ali Babar

Focus: Cost Optimization and Operational Overhead Elimination Technology: AWS Lambda / Google Cloud Run / Azure Functions Before Cloud After Cloud Serverless Architecture High Idle Costs: Paying for servers 24/7/365, even during low-traffic nights/weekends. Pay-Per-Execution: Only charged for the actual milliseconds of compute time used. High Maintenance Burden: Required constant patching and management of server […]

Focus: Lift-and-Shift for Cost Reduction & Performance Stability Technology: Azure Virtual Machines / AWS EC2 Before Cloud After Cloud Migration High Capital Expenditure (CapEx): Required major hardware refresh every 4-5 years. Operational Expenditure (OpEx): Pay-as-you-go model eliminated CapEx on infrastructure. Low Uptime: Struggled to maintain 99.5% uptime due to single point of failure. High Uptime:

Our transition focuses on four critical areas that directly benefit you: Blazing-Fast Global Performance with CDN We utilize a Content Delivery Network (CDN)—such as Google Cloud CDN or Amazon CloudFront—to fundamentally change how our content is delivered. A global network of servers (or points of presence) that caches static assets (images, videos, stylesheets) closer to

Virtual Machines (IaaS) for Seamless Migration For foundational systems and dedicated workloads, we utilize Virtual Machines (VMs)—such as Compute Engine (GCP) or EC2 (AWS). These services represent the strategic starting point for website modernization.Migration and Control We moved existing, reliable infrastructure to the cloud quickly, gaining immediate benefits like better control over the environment and

Virtual Machines (IaaS) Compute Engine (GCP), EC2 (AWS), Azure Virtual MachinesMigration/Rehosting: Move existing website infrastructure to the cloud for better control and immediate cost savings over traditional hosting. Containerization (PaaS/CaaS) Google Kubernetes Engine (GKE), Amazon ECS/EKS, Azure Kubernetes Service (AKS) Modernization/Scalability: Run complex applications (e.g., e-commerce, custom apps) with massive, automated scaling and better resource

Challenge Deliver low-latency, privacy-aware people and object analytics across a fleet of Raspberry Pi edge devices, with enriched demographic insights and centralized observability, while ensuring maintainability and continuous deployment. Objectives Solution Overview Technology & Methods Key Innovations Outcomes Impact Enhanced situational awareness, improved utilization insights, and scalable foundation for future analytic modules. Future Extensions Summary

Overview Imagine saying a single wake word and your car instantly listens. A client asked us to build that seamless voice activation experience. We engineered a lightweight ML model that reliably detects a chosen trigger word in natural speech and can be rapidly retrained to a new word using minimal fresh audio, reducing data collection