Craftwork

Client

A healthcare data analytics company delivering SaaS-based solutions and data services to pharmaceutical, medical, and research industries.

Overview

This healthcare data analytics company needed a robust, flexible infrastructure that could support the development and deployment of advanced machine learning (ML) algorithms and handle vast amounts of data securely. With a mission to deliver impactful insights and high-quality data services, the client needed an environment that would empower their data science and data engineering teams to operate at their full potential.

Craftwork partnered with the client to design, deploy, and manage up to 10 Kubernetes clusters using AWS EKS (Elastic Kubernetes Service). The clusters facilitated a seamless, scalable development environment where data science teams could build complex ML models and data engineering teams could operationalize those models across various SaaS solutions for their healthcare partners.

Challenges

1. High-Compute, Scalable Environment for Machine Learning: Data science teams required powerful computing resources to train and refine ML algorithms. Traditional infrastructure was restrictive and lacked the flexibility to scale on-demand.
2. Complex Data Pipeline Management: Data engineering teams needed to process and manage massive data pipelines, ensuring data was securely ingested, transformed, and ready for analysis. With multiple clusters, orchestrating data movement and model deployments had to be seamless and efficient.
3. Regulatory Compliance: Given the sensitive healthcare data involved, stringent security and compliance measures were essential to meet HIPAA, GDPR, and other industry standards. This was particularly challenging in a multi-cluster, multi-tenant environment.
4. Efficient Resource Allocation: Managing up to 10 Kubernetes clusters introduced complexities around resource allocation, monitoring, and cost management, necessitating careful orchestration and oversight.

Solution

Craftwork designed a multi-cluster Kubernetes architecture on AWS EKS, addressing the client’s needs for scalability, compliance, and high performance. 1. Cluster Design & Management: We structured the environment into distinct Kubernetes clusters for different departments and use cases, including development, staging, and production environments for both data science and data engineering teams. This approach provided the flexibility to manage workloads independently, allowing each team to operate without impacting others and isolating environments for enhanced security. 2. Dynamic Scaling and Resource Management: AWS EKS’s autoscaling capabilities enabled each Kubernetes cluster to dynamically adjust resources based on demand. For ML workloads, GPU-enabled nodes were provisioned, allowing data scientists to run high-compute algorithms and quickly scale down when not in use, optimizing costs. The data engineering teams used managed node groups with optimized scaling policies to handle high-throughput data pipelines. 3. Enhanced Data Security and Compliance: With Craftwork’s guidance, the client implemented best-practice security configurations within each EKS cluster. We leveraged AWS’s built-in security features, such as IAM roles for service accounts, VPC isolation, and EKS-managed worker nodes to enforce strict access controls. Network policies were implemented to restrict data flows, ensuring compliance with HIPAA and GDPR regulations. 4. Streamlined Model Deployment & CI/CD Pipelines: We integrated continuous integration and continuous deployment (CI/CD) pipelines using tools like Jenkins and GitLab CI for automated model deployment. This enabled data scientists to push trained models to staging and production environments with minimal friction, accelerating the time-to-market for new algorithms. Kubernetes Jobs and CronJobs were employed for batch data processing and scheduling periodic model retraining, providing a seamless workflow from development to deployment. 5. Centralized Monitoring and Cost Management: Craftwork set up a centralized monitoring solution using Prometheus and Grafana, enabling real-time visibility into cluster health, resource utilization, and application performance across all clusters. AWS CloudWatch and Cost Explorer were configured for proactive alerting on resource usage and cost monitoring, ensuring that each department could maintain visibility into their budgets and optimize resource allocation.

Impact

The implementation of multiple Kubernetes clusters on AWS EKS transformed the company’s data science and data engineering capabilities. Key outcomes included: • Increased ML Model Development Speed: With dedicated, scalable environments, data scientists could train and iterate on ML models faster and more efficiently. Model development time decreased by over 50%, allowing the team to release new algorithms and enhancements more frequently. • Seamless Model Deployment for Data Engineering: Data engineering teams benefited from an environment optimized for high-throughput data processing, with well-orchestrated data pipelines and straightforward model deployment processes. This enabled the company to launch several new SaaS data services tailored to pharmaceutical and research clients, resulting in a 30% increase in product offerings within the first year. • Scalability & Cost Efficiency: By utilizing EKS autoscaling features, the client optimized resource usage across clusters, leading to a 40% reduction in infrastructure costs. The ability to automatically scale clusters up or down based on demand helped control costs while maintaining performance. • Improved Compliance and Security: The security measures in place provided peace of mind around data compliance. AWS’s security offerings, coupled with Craftwork’s best practices, ensured that all data remained protected and the infrastructure adhered to regulatory requirements, reducing the risk of non-compliance fines or data breaches.

Conclusion

Through Craftwork’s expertise in Kubernetes and AWS EKS, this healthcare data analytics company gained a high-performance, scalable infrastructure that empowered its data science and data engineering teams. With the ability to manage up to 10 Kubernetes clusters efficiently, the company could focus on its mission of delivering high-impact data solutions to the healthcare sector.

This multi-cluster Kubernetes architecture on AWS EKS has positioned the client to continue scaling their capabilities and stay at the forefront of healthcare data innovation, all while controlling costs and maintaining a robust security posture.

Craftwork remains a committed partner, providing ongoing management and optimization to ensure the infrastructure adapts as the company’s needs evolve.