Startups Using CloudFormation in Boston
Via their job posts and information submitted by startups themselves, these are the Boston CloudFormation startups we've found.
Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.
Predictive analytics for healthcare data, targeting preventable admissions, member retention, and risk-based reimbursement eligibility.
Tech Stack Highlights
Machine Learning – We build models on our Spark platform using MLlib as well as in custom Python environments where we use many of the popular Python-based machine learning libraries. We’ve invested the most in using the Pytorch library, which we use for our deep learning models.
Spark & Scala – We use a Scala-based data pipeline hosted on Spark to ingest customer data and prepare it for use in our models.
Zeppelin & Jupyter – We work with data using Zeppelin notebooks for Spark and Jupyter in our Python environments.
Automation & Infrastructure – We use CircleCI to build and deploy both our services and infrastructure. We use AWS Lambda to automate infrastructure tasks and create custom notifications and alerts to simplify our internal workflows.
AWS – We host our infrastructure on AWS. We’ve built an independently audited platform that supports working with protected health information.
3D printing of metal parts.
Marketplace for alcohol retailers and distributors.
Platform for privately building & testing financial trading algorithms.
Tech Stack Highlights
Flask – we migrated to Flask from Django to increase the flexibility with which we build and manage our portfolio of workflow applications. We manage our own library of plugins and cookiecutters to enable efficient setup for new team members and team members picking up new projects.
React – we moved to React from Ractive as our JS framework for its performance, component oriented architecture, and server side rendering. React raises the level of abstraction in our front-end code base, making it more predictable and allowing our engineers to focus on building functionality versus wrestling with the framework.
AWS – as a healthcare IT company, we need to maintain a very high bar for our security and privacy infrastructure, given the high-stakes nature and stringent requirements of our large hospital system customer base. Our users rely on our ProviderMatch platform to match millions of patients to the right providers every year. AWS is our core cloud platform that allows us to accomplish this at scale.
ElasticSearch – our core patient-provider matching engine is enable by multi-faceted search. We have customized ES to our domain-specific data models, query types, and end user stories. ES recently migrated away from search as a core area of innovation (in lieu of analytics), so while we are investing in ways to optimize our utilization of ES, we also continue to keep an eye on the landscape of alternatives!
Slack – Slack is core to how our team communicates and gains critical insight into how our platform is performing. The ProviderMatch platform’s services are tightly integrated into Slack, and provide notifications and real-time performance insights that allow our team to stay on top of all platform activity.