GxP-Compliant, Scalable Data Science Platform Migration and Integration

The client’s existing data science platform has become outdated due to new regulatory GxP guidelines, posing compliance and data integrity risks. Limited support for modern deployment environments like Kubernetes hampers scalability and operational flexibility. Multiple inconsistent environments increase maintenance complexity and support workload, while limited staff expertise in best practices for dependency management, debugging, and version control further complicate system updates and validation processes.

A large pharmaceutical organization with diverse data science environments seeking to upgrade regulatory compliance, improve operational efficiency, and enable scalable infrastructure integration.

• Achieve full GxP compliance and audit readiness for the data science platform.
• Implement a scalable, futureproof environment integrated with Kubernetes for dynamic resource management.
• Standardize and consolidate multiple environments to reduce maintenance overhead and improve consistency.
• Develop reproducible, traceable automation scripts for system installation, configuration, and deployment.
• Enhance team capabilities through knowledge transfer on modern tools and infrastructure management practices.
• Reduce support workload and streamline change management processes.

• Migration of existing data science applications and user data into a new, compliant environment with minimal disruption.
• Automation of installation and configuration processes using infrastructure-as-code tools (e.g., Ansible).
• Integration of the environment with Kubernetes for resource scaling and management.
• Merging multiple disparate environments into a unified, standardized platform.
• Provision of validation and qualification documentation to support compliance audits.
• Knowledge transfer sessions on automation tools, containerization, and orchestration to empower internal teams.

• Internal data repositories
• Application deployment pipelines
• Compliance validation and documentation tools

• Environment must be fully GxP compliant and audit-ready.
• Scalable to accommodate increased workload demands without performance degradation.
• Automated scripts must ensure high reproducibility and traceability.
• Migration process should not disrupt ongoing data science activities.
• Supporting documentation and validation evidence must meet regulatory standards.

The project will enable a GxP-compliant, scalable environment that reduces regulatory and operational risks. Implementation of automation and standardized infrastructure will significantly lower support workload, enhance agility, and future-proof the platform. Data science teams will experience minimal workflow disruption, with increased consistency across environments, leading to improved productivity and easier compliance audits.