Startups Using Docker in Boston
Via their job posts and information submitted by startups themselves, these are the Boston Docker startups we've found.
Interested in other technologies? Browse or search all of the built-in-boston tech stacks we've curated.
Next-gen natural language processing for customer feedback & AI interaction.
Tech Stack Highlights
Postgres & Redis – All this is backed up by RDS instances in AWS running PostgresDB. We heavily use Redis and SOLR for data caching and queue management.
Flask/Python – The rest of our apps and services – Email systems, data analysis, internal tools – all run in Python based Flask/Flask-Restless environments.
ELK – Our logging system is run as an Elasticsearch-Logstash-Kibana stack utilizing Filebeat and Logspout for streaming the log output. From this stack we’ve also created a comprehensive Technical SEO Dashboard where we can monitor crawlers and their activity and measure the cause & effect on new site features.
DevOps – Our apps are deployed using Docker Swarm orchestration via Ansible scripts for independence from specific cloud providers. We’ve built a structure with Docker in a Blue/Green deployment methodology so there is zero downtime when releasing code updates. The system is front ended with Jenkins-CI for automated execution of Unit/Integration/Acceptance test suites.
Podcast discovery and listening platform. Building “the new radios of our time.”
“Smart contracts and distributed ledger solutions built for modern finance.”
“Brand-safe” UGC video content. Platform for brands to enable user-generated video with brand filters on top. Users can share on existing social channels but brands can remove content they don’t like.
Web and mobile app for children with autism; built to allow parents to use video clips relevant to their child’s passions to provide social, emotional, and other lessons.
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.