Relation Therapeutics adopted a standardised MLOps framework on Amazon SageMaker to streamline ML workflows, enhance model governance, and accelerate scientific discovery with scalable, secure, and reproducible infrastructure.

The Challenge

Relation Therapeutics needed to modernise and scale its machine learning workflows to accelerate drug discovery while maintaining governance and reproducibility.

• Fragmented ML workflows built on ad-hoc scripts and unmanaged infrastructure slowed research progress.
• Manual processes across data preprocessing, model training, and deployment led to inefficiencies and reproducibility challenges.
• Lack of standardised governance limited auditability, model traceability, and regulatory compliance.
• Collaboration across research teams was hindered by inconsistent tooling and access controls.

Our Solution

A standardised MLOps framework on SageMaker was proposed to automate and govern Relation Therapeutics’ ML workflows:

• End-to-end automation using SageMaker Pipelines for data prep, training, evaluation, and deployment
• Integrated data flow via Amazon S3, AWS Glue, and Feature Store for consistent, reusable features
• Governed ML lifecycle with Model Registry, Amazon CloudWatch, and AWS CloudTrail for versioning, monitoring, and audit
• Secure, scalable operations with IAM-based access control and real-time/batch model deployment

The Result

The implementation of standardised MLOps on SageMaker has enabled Relation Therapeutics to achieve:

• 3x faster model development cycles, reducing iteration time from months to weeks
• 70% improvement in model reproducibility and auditability through automated, versioned pipelines
• 50% reduction in manual effort via automated data preprocessing, training, and deployment
• Improved model performance tracking using integrated monitoring and evaluation tools
• Enhanced compliance and governance with a centralised model registry and audit logging
• Seamless collaboration across teams with secure, role-based access to ML workflows

The post Relation Therapeutics: Scaling Machine Learning with Amazon SageMaker MLOps appeared first on Rambunct Consulting.

Architected and deployed a container-based microservices platform using Amazon ECS to process healthcare claims data, implementing service discovery, automated deployments, and container optimisation for high-throughput data processing.

The Challenge

• High-volume healthcare data processing requirements
• Complex microservices architecture with multiple interdependent services
• Need for automated container deployment and scaling
• Requirements for high availability and fault tolerance
• Container resource optimisation and cost management
• Compliance with healthcare data security standards

Our Solution

• ECS Cluster Optimisation: Deployed multiple ECS clusters across dev, staging, and prod environments using a mix of Amazon EC2, AWS Fargate, and Spot Instances for cost-effective scaling.
• Advanced Orchestration: Enabled Amazon CloudWatch-based auto-scaling, service discovery via AWS Cloud Map, Application Load Balancer integration, and blue/green deployments for seamless updates.
• Secure Networking Design: Configured VPCs with private subnets, granular IAM roles, AWS PrivateLink, and security groups for secure, isolated container operations.
• Monitoring & Observability: Implemented Container Insights, centralised logging via FireLens, custom CloudWatch dashboards, and AWS X-Ray for end-to-end visibility.

The Result

The ECS-based implementation delivered high availability, operational efficiency, and technical excellence across performance, automation, and resource optimisation.

• High ECS Performance: Achieved 99.99% uptime, sub-second deployments, and seamless auto-scaling during 3x traffic spikes with 40% less resource usage.
• Operational Efficiency: Enabled zero-downtime deployments, automated health checks, and reduced deployment time from hours to minutes with self-healing capabilities.
• Technical Enhancements: Reduced container image size by 60%, ensured sub-100ms service discovery, and optimised task placement for better resource utilisation.
• Strengthened Security: Applied least-privilege IAM roles for ECS tasks, enhancing the overall security posture of the environment.

The post Scalable Container Orchestration with Amazon ECS for Healthcare Data Processing appeared first on Rambunct Consulting.