Scientific software infrastructure gets very complex, very fast. Your application may include databases, Dask or Spark clusters.
If you need more customization, or just prefer to maintain control over your deployment, deploying your RShiny application to AWS on Kubernetes is an ideal solution for you.
This solution gives you complete control. You choose the power of the machines you deploy to, the number of instances to deploy for autoscaling and load balancing, along with mixing and matching other services you need.
We will go over your requirements, including databases, Spark/Dask Clusters, filesystems, etc, to fit your needs, either email or over a Zoom call.
Once the requirements are set these will be coded into a Terraform Recipe, along with scripts for building, deploying, and testing your application's ecosystem that can be uploaded to any CI/CD platform of your choosing (CircleCI, AWS CodeCommit, Travis, etc).
All the code and configuration you receive is completely, 100% yours and without any intellectual property. Each deployment comes with a 1 month support contract for tweaking parameters and keeping up with API changes.
That's totally fine. ;-) I have several guides to get you started, including:
Fill out the form below to get in touch and your preferred method to go forward.
Over the course of my career, I have earned a robust reputation for outstanding genomics and bioinformatics DevOps, and I am known for my ability to design and integrate innovative, flexible infrastructures, leveraging in-depth client and business consultation to uncover critical, unique program needs. Throughout the years I’ve seen datasets grow in size and complexity (who remembers microarrays?) and worked with researchers to develop analysis infrastructure to accommodate the ever-growing demand for more number crunching.
I have consulted with the Bioinformatics Contract Research Organization (CRO) and BitBio to design and deploy a major manual-labor saving HPC cluster with integrated SLURM scheduler and user / software stacks, and elastic computational infrastructure for genomics analysis, empowering a greater focus on high-priority projects and activities.
I also designed and deployed complex data visualization applications on AWS such as NASQAR. I am both a contributor and core team member of Bioconda as well as a contributor to the Conda Ecosystem and EasyBuild.