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Learn how automation and DevOps help life sciences and biotech teams reduce manual work, improve software delivery, automate cloud infrastructure, strengthen security, and support faster, more reliable operations.
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PTP Solves: Migrating AWS Lambda Runtimes for Secure, Compliant Biotech ApplicationsAs biotech and pharmaceutical research organizations increasingly adopt cloud-based solutions to accelerate data processing and analysis, the tools that support these workflows must...
Discover the key aspects of mastering AI architecture in the life sciences industry with expert insights from Aaron Jeskey, Senior Cloud Architect at PTP. This blog post explores the innovative approaches and strategies used to leverage AI for scientific advancements.
In the high-risk domain of biotechnology, PTP partnered with a life sciences client to automate critical data pipelines using AWS, Terraform, and Nextflow, ensuring research validity, cost optimization, and operational efficiency. This collaboration led to the implementation of automated, repeatable, and secure workflows, allowing the client to focus on advancing scientific discovery and improving patient outcomes.
In this presentation, Scott Scheirey, Scientific Partner Advisor at PTP, addresses common challenges faced by computational biologists in optimizing bioinformatics workflows. He highlights the use of AWS Batch, Nextflow, and Airflow to enhance pipeline efficiency, reliability, and speed. Scheirey explains how these tools can help process large volumes of genomic data more quickly and cost-effectively, ultimately supporting research validation and clinical trials.
View PTP’s case study on implementing Infrastructure as Code (IaC) solutions for a life sciences startup. The focus is on creating a scalable, automated, and secure data processing environment using AWS services and tools such as EC2, Lambda, and Terraform. The goal is to streamline research validation, optimize costs, and ensure data security.
Discover how Benchling and PTP CloudOps can overcome data management challenges in life sciences by standardizing and optimizing scientific workflows.
Explore the advantages and practical scenarios for using Apache AirFlow and NextFlow for data pipelines. This article delves into how AirFlow excels in managing complex workflows, orchestrating ETL jobs, and supporting machine learning data preparation on AWS, while also highlighting the capabilities of NextFlow for scientific workflows.
