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Resolving Healthcare Worker Shortage through AI
Large Scale Cloud Infrastructure for Healthcare LLM
Large Scale Cloud Infrastructure for Healthcare LLM

The Challenge

Developing advanced AI solutions for healthcare requires managing complex infrastructure, stringent compliance standards, and operational efficiency. Our client, an innovative provider developing a healthcare-focused Large Language Model (LLM), faced substantial challenges in DevOps and MLOps. The project’s success hinged on effectively managing hybrid cloud environments, supporting extensive AI model training, ensuring secure and compliant deployments (HIPAA, SOC2, HITRUST), optimizing infrastructure costs, and integrating sophisticated voice recognition technologies.

The Discovery

Collaborating closely with the client’s team, Conrad Labs identified several key areas to address:

  • Hybrid cloud infrastructure for AI model training and deployment.
  • Robust voice technology integration.
  • Advanced security and compliance requirements.
  • Efficient automation, scalability, and cost management.

By leveraging industry best practices and innovative technology, Conrad Labs developed a strategic approach to meet these demands.

The Outcome

Conrad Labs implemented a comprehensive solution utilizing AWS and Google Cloud Platform (GCP), containerization with Docker and Kubernetes, and CI/CD pipelines using GitHub Actions, AWS CodePipeline, and Terraform:

  • Enhanced Model Training Infrastructure:
    Transitioned training from Slurm to a hybrid Kubernetes-based model across AWS and GCP, significantly improving efficiency and reducing downtime.

  • Reliable Deployment and Voice Integration:
    Transitioned training from Slurm to a hybrid Kubernetes-based model across AWS and GCP, significantly improving efficiency and reducing downtime.

    Transitioned training from Slurm to a hybrid Kubernetes-based model across AWS and GCP, significantly improving efficiency and reducing downtime.

  • Integrated GPU-accelerated, custom-hosted ASR models.

    • Robust Security and Compliance:

    • Implemented rigorous security tooling, including Tenable, AWS Inspector, AWS Guardduty, Security Hub, and IAM.
    • Achieved full compliance with healthcare-specific regulations (HIPAA, SOC2, HITRUST), simplifying audit processes.

Further Observations

Cost Optimization Initiatives:

  • Proactively terminated unnecessary cloud instances, introduced comprehensive tagging policies, and conducted regular rightsizing audits, significantly reducing operating costs.
  • Successfully archived petabytes of redundant data, optimizing storage expenses.

Scalability and Observability:

  • Established robust disaster recovery and backup strategies, comprehensive incident response plans, and systematic capacity planning to ensure infrastructure scalability and resilience.
  • Enhanced observability using tools like Sumologic, Prometheus, Grafana, CloudWatch, and Sentry for proactive monitoring and rapid troubleshooting

Conclusion

The collaboration between Conrad Labs and the client resulted in a secure, scalable, and highly efficient infrastructure, pivotal for advancing AI capabilities in healthcare. Through strategic use of cloud-native technologies, rigorous compliance practices, and meticulous cost optimization, the project successfully transformed complex AI infrastructure challenges into sustainable operational solutions. This case exemplifies the powerful impact of combining technical innovation with strategic planning to drive advancements in healthcare technology.

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address

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Kickstart 4th Floor, 
4-A, Block 6, PECHS, Shara -e- Faisal, Karachi

address

USA

166 Hargraves Drive
Suite C-400-349
Austin, TX 78737

Pakistan

50-N Gurumangat Rd, Block N Gulberg 2, Lahore, Punjab

Kickstart 4th Floor, 4-A, Block 6, PECHS, Shara-e-Faisal
Karachi, Pakistan 75350

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