How PTP Boosted Biotech Productivity by 10% with AWS SageMaker* in a Regulated Environment
For early and mid-stage Biotechnology and Life Sciences companies, easily accessing all sources of data—whether on-prem from the lab or in the cloud—is critical for rapidly analyzing new molecules and assessing the validity of new drugs or treatments. Data teams often work in hybrid, remote, and asynchronous environments, requiring tools that enable collaboration and maintain version control. Additionally, these companies frequently handle sensitive individual and private health information, demanding robust security and compliance measures.
- *Resulted in a 10% productivity increase for data scientists by providing version-controlled, collaborative machine learning environments.
The Challenge
A PTP client, dedicated to modifying medicines by identifying validated targets and advancing multiple therapeutic modalities, sought an improved method for collaborative version control of data in AWS. They needed a solution that could support dozens of computational biologists leveraging Amazon SageMaker’s machine learning models to accelerate data analysis.
The Solution
PTP architected a tailored solution to address the client’s challenges, deploying a collaborative environment powered by Amazon SageMaker:
Custom Docker Images and PyPI Packages
Deployed custom Docker images with private Python Package Index (PyPI) packages into SageMaker to enable secure collaboration.
Created pre-defined environments with specific Python packages for seamless integration.
Shared Storage Access
Integrated shared storage for on-prem, third-party CRO, and Amazon S3 data, enabling seamless access to Jupyter notebooks across users.
Custom IPython Kernels
Built custom IPython kernels for interacting with SageMaker Studio’s Jupyter notebooks, ensuring standardized working environments.
Kernels were deployed in Docker containers, automatically pulled by SageMaker during notebook instance creation.
Enhanced Data Security
Designed and deployed a private PyPI server to secure Personal Identifiable Information (PII) and ensure regulatory compliance.
Configured the environment to integrate with the company’s Single Sign-On (SSO) and Multi-Factor Authentication (MFA), following best security practices.
The AWS SageMaker architecture deployed for collaborative machine learning environments in a biotech use case, including shared storage, secure authentication with Okta, and integration with Docker containers from ECR.
The Outcome
PTP’s expertise in biotechnology workflows, Amazon SageMaker, and machine learning allowed for the successful deployment of a collaborative data science environment:
10% Productivity Increase
Improved productivity for data scientists through version-controlled, collaborative machine learning environments.
Accelerated Medicine Development
Faster analysis and iteration supported the development of neurogenetic medicines.
Robust Security and Compliance
Ensured data integrity and privacy through stringent security measures, meeting regulatory requirements.
By enabling collaborative data analysis and secure machine learning workflows, PTP’s solution positioned the client to accelerate drug development and enhance scientific innovation.
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