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Lift-and-Shift Cloud Modernization for an Aerial Imagery Enterprise
A leading aerial imagery platform modernized fragmented bare-metal infrastructure into a scalable AWS-native ecosystem. The shift delivered significant cost efficiency and accelerated innovation at scale.
At a glance
Industry
Aerial Imagery & Geospatial Data Platforms
Location
United States
Successive Highlight
30 PB
Zero-loss imagery migration
45 Nodes
EKS modernization at scale
About Client
With over 25 years of expertise in aerial imagery, geospatial data processing, and large-scale image analytics, the client evolved through multiple acquisitions into a highly diversified technology ecosystem. This rapid expansion introduced fragmented infrastructure across container platforms, storage environments, and processing systems, creating operational complexity at scale. As the need for AI-powered image processing, high-performance computing, and scalable data infrastructure accelerated, the company embarked on a modernization journey to improve agility, streamline operations, and support next-generation innovation.
About this challenges
A leading aerial imagery enterprise struggled with fragmented infrastructure spanning multiple platforms (Docker, Kubernetes, Rancher) and storage systems, managing 32+ PB of data. Fixed-capacity resources led to $1.8M annual waste, while GPU shortages caused long ML training cycles: limited visibility, integration issues, and high operational overhead restricted scalability, performance, and innovation.
Solutions Implemented
A phased AWS cloud modernization strategy was executed to migrate bare-metal infrastructure and unify fragmented platforms. This enabled scalable ML workloads while reducing cost and operational complexity.
Cloud Migration
Migrated workloads from bare-metal Kubernetes clusters to Amazon EKS to improve scalability, resilience, and operational efficiency.
Architecture Design
Designed a three-layer cloud architecture integrating storage, AI/ML processing, and applications to streamline performance and scalability.
Data Migration
Executed large-scale migration of 30 PB data using AWS DataSync, Snowball, and Velero with zero data loss and minimal disruption.
ML Platform Enablement
Built a scalable ML platform using EKS and SageMaker to accelerate model training, inference, and experimentation cycles.
Elastic Compute Scaling
Enabled dynamic GPU and CPU scaling using Karpenter to efficiently handle fluctuating workloads and reduce infrastructure overhead.
Storage Modernization
Transitioned legacy storage systems to Amazon S3 and EBS to optimize cost, improve accessibility, and enhance performance.
DevOps Automation
Implemented CI/CD pipelines using GitHub Actions and ArgoCD to ensure faster, consistent, and reliable software delivery.
Observability Framework
Established centralized monitoring, logging, and alerting to enable proactive performance management and system reliability.
Enterprise Integration
Integrated core enterprise systems using API-driven architecture to enable seamless data flow and operational continuity.
Security & Governance
Implemented secure access, authentication, and governance controls to ensure compliance, data protection, and platform security.
Outcome
Innovation-Driven Cloud Platform for ML Workloads and Intelligent Automation
Cloud-native platform enabled high-performance ML workloads with on-demand scalability.
Optimized infrastructure reduced operational overhead and improved cost efficiency.
Accelerated ML training and inference improved speed, productivity, and experimentation.
Enhanced monitoring and automation strengthened reliability, governance, and system performance.
“Successive Digital helped us modernize a highly complex infrastructure into a scalable, cloud-native platform without disrupting operations. Their deep expertise in cloud, Kubernetes, and ML transformation significantly improved our performance, reduced costs, and accelerated innovation. We can now scale efficiently, run advanced AI workloads, and respond to business demands with far greater agility.”
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