Executive Summary
An AI-powered analytics startup needed production-grade AWS EKS infrastructure to run containerized microservices, real-time data pipelines, machine learning workloads, and a full CI/CD deployment workflow. The company had no cloud environment in place. The platform required three isolated Kubernetes environments, multiple database engines, serverless APIs, streaming data ingestion, NLP and image analysis services, and enterprise-grade security and monitoring.
Teleglobal designed and deployed the complete architecture from scratch. The project covered Amazon EKS clusters for Production, Testing, and QA, backed by DynamoDB, RDS PostgreSQL, ElastiCache Redis, OpenSearch, serverless services (Lambda, API Gateway), real-time streaming (Kinesis, Glue, Athena), ML services (SageMaker, Comprehend, Rekognition), CI/CD with Jenkins and SonarQube, and observability through Grafana, Prometheus, and CloudWatch. This engagement is part of Teleglobal’s Expert AWS Solutions practice.
Background
The client is a startup building an AI-powered platform that processes large volumes of data through machine learning models, NLP analysis, and image recognition to deliver intelligent insights to enterprise customers. The platform runs as a set of containerized microservices that need to scale independently, communicate through message queues and event-driven triggers, and process streaming data in real time.
The team needed a full AWS cloud platform built from scratch. This was not a simple hosting setup. The requirements spanned container orchestration across three isolated environments, multiple database engines for different workload patterns, serverless APIs, a real-time data ingestion pipeline, machine learning infrastructure, automated CI/CD, and full observability with dashboards and alerting.
The Challenge
No Cloud Infrastructure
The company had no production environment on AWS. Everything from EKS clusters to databases, networking, security, serverless services, and monitoring needed to be designed and deployed from scratch.
Complex Microservices Architecture
The platform runs as many containerized microservices that need workload isolation between Production, Testing, and QA. Each environment requires its own EKS cluster with dedicated worker nodes, container registry access, and independent scaling.
Real-Time Data and ML Requirements
The platform processes streaming data that needs to be ingested, transformed, and made queryable in near real time. On top of that, machine learning models need infrastructure for training and deployment, and the application relies on NLP and image analysis services for intelligent features.
Enterprise Security and Observability
AI workloads handling enterprise customer data demand proper network isolation, encryption at rest and in transit, least-privilege access, WAF protection, and centralized monitoring with proactive alerting. The company needed all of this from day one.
The Solution
Teleglobal built the full AWS EKS infrastructure with databases, serverless services, data pipelines, ML stack, CI/CD automation, and observability. This project was delivered as part of Teleglobal’s cloud managed services.
Amazon EKS Deployment with Three Isolated Environments
Three Amazon EKS clusters were provisioned with dedicated worker nodes for Production, Testing, and QA. Each cluster runs in private subnets with workload isolation, independent scaling, and efficient resource utilization. Amazon ECR stores, manages, and versions all Docker container images used by EKS workloads.
Multi-Database Architecture
Different workloads need different database engines. The architecture includes DynamoDB for metadata and high-performance transactional workloads, Amazon RDS for PostgreSQL with automatic failover and backup retention, ElastiCache Redis as an in-memory caching layer to reduce database latency, and Amazon OpenSearch with Kibana for centralized log analytics, search, and visualization.
Serverless APIs and Event-Driven Architecture
Amazon API Gateway exposes backend services and provides secure API-based access to applications running on Lambda and EKS. AWS Lambda handles event-driven functions without provisioning servers. Amazon SQS decouples microservices through asynchronous message queuing. Amazon SNS handles notifications, alerts, and inter-service fan-out patterns. Amazon EventBridge automates task orchestration and service-based event triggers across components.
Real-Time Data Pipeline
A real-time streaming pipeline was built using Amazon Kinesis Data Streams and Kinesis Firehose to ingest and deliver data into S3 and OpenSearch for analytics. AWS Glue ETL jobs transform ingested data and register metadata in the Glue Data Catalog. Amazon Athena provides serverless SQL analytics on S3 datasets with cost-optimized querying.
Machine Learning and AI Services
Amazon SageMaker was provisioned for training, deploying, and operationalizing ML models. Amazon Comprehend performs NLP analysis and Amazon Rekognition handles image and video analysis for intelligent application features.
CI/CD Pipeline with Jenkins and SonarQube
Jenkins was configured for CI/CD automation with SonarQube for code scanning and quality checks. Build workflows push container images to ECR and deploy applications to EKS clusters. This gives the development team automated, secure, and repeatable deployments from code commit to production.
Security and Access Controls
A layered security approach was implemented following AWS security best practices:
- AWS WAF protecting applications from malicious requests and unauthorized traffic
- AWS IAM with least-privilege policies, user groups, and service-specific roles
- VPC with Public, Private Application, and Private Data subnets for full network isolation
- OpenVPN for secure remote access to private subnets
- AWS KMS for encryption at rest and in transit across all services
- AWS Secrets Manager and Parameter Store for secure credential and configuration management
Monitoring and Observability
Grafana and Prometheus provide application and infrastructure monitoring with performance dashboards and alerting. OpenSearch with Kibana delivers centralized log analytics. Amazon CloudWatch handles log aggregation, metrics collection, and proactive alerting across all AWS services.
AWS Services Used
| Category | AWS Services |
|---|---|
| Container & Compute | Amazon EKS (3 clusters), Amazon EC2, Amazon ECR |
| Databases & Caching | DynamoDB, Amazon RDS PostgreSQL, ElastiCache Redis, Amazon OpenSearch + Kibana |
| Serverless & Messaging | API Gateway, AWS Lambda, Amazon SQS, Amazon SNS, Amazon EventBridge |
| Data & Analytics | Kinesis Data Streams, Kinesis Firehose, AWS Glue, Amazon Athena, Amazon S3 |
| AI & Machine Learning | Amazon SageMaker, Amazon Comprehend, Amazon Rekognition |
| CI/CD & DevOps | Jenkins, SonarQube, Amazon ECR |
| Monitoring & Observability | Grafana, Prometheus, Amazon CloudWatch, OpenSearch + Kibana |
| Networking | Amazon VPC, ALB, NAT Gateway, OpenVPN, Security Groups, NACLs |
| Security | AWS WAF, AWS IAM, AWS KMS, Secrets Manager, Parameter Store |
Results
| Area | Before | After |
|---|---|---|
| Infrastructure | No cloud environment | Full AWS platform with EKS, databases, serverless, data pipelines, and ML stack |
| Container Orchestration | No Kubernetes | 3 EKS clusters (Prod, Test, QA) with ECR and worker nodes |
| Data Pipeline | No streaming capability | Real-time pipeline with Kinesis, Glue ETL, Athena, OpenSearch |
| AI/ML | No ML infrastructure | SageMaker, Comprehend, and Rekognition deployed |
| CI/CD | No automated deployment | Jenkins + SonarQube pipeline deploying to EKS via ECR |
| Monitoring | No observability | Grafana, Prometheus, CloudWatch, OpenSearch + Kibana dashboards |
| Security | No security controls | WAF, IAM least privilege, KMS encryption, Secrets Manager, VPN |
What’s Next
- Implement Terraform-based Infrastructure as Code for repeatable cluster and service provisioning
- Add auto-scaling policies for EKS worker nodes based on real-time workload patterns
- Expand ML pipelines with SageMaker Pipelines for automated model training and deployment
- Set up multi-account AWS governance using AWS Organizations and Control Tower
- Adopt AWS cost monitoring with Cost Explorer and Budgets across all environments
- Build operational runbooks and incident response procedures for production support
About Teleglobal International
Teleglobal International is an IT consulting company that helps AI startups and technology companies build production-grade AWS EKS infrastructure for containerized workloads, real-time data pipelines, and machine learning. From AWS solutions and cloud security to cloud managed services, Teleglobal builds cloud platforms that are secure, scalable, and ready for AI-powered growth. Explore more client success stories.