I’m Rick Sullivan. I’m an engineer at FocusVision, working in my hometown of Portland, OR.
My current interests are natural language processing, kombucha brewing, and machine learning that solves unimportant problems.
Reach out to me at [email protected], or check out my professional experience on my resume.
I’ve had the pleasure of working with GraphQL in some of our more recently built applications. Developing frontend applications backed by a GraphQL API is the most productive my team and I have ever been. The flexibility provided lets you try out a variety of UX solutions to your problem, without any additional work to restructure your backend.
About a year ago, Amazon publicly released Sagemaker, its platform for training and managing machine learning models. Sagemaker has an appealing value proposition–users can train models and tune hyperparameters using on-demand EC2 instances with a variety of hardware options, and models are stored for deployment to endpoints with IAM access control. On my team, we chose Sagemaker for managing our training and deployment of a text classification algorithm using a Tensorflow neural network. Here are some of the things I learned while creating an automated path to production.
In a previous post about Docker for Mac,
I described a way to make SSH keys available inside of Docker containers–here’s
a simpler approach with a few advantages over my previous suggestion. Docker
volumes allow you to link directories from your host machine to the Docker
container, even if the host location is outside of the directory with your
docker-compose.yml
.
Want to add a contact form to your simple static site? Here’s how to do it with near-zero costs, easy setup, and easy teardown if you want to replace the backend with your own server-based application.
Maybe you’ve seen the slick “Magic Link” login that Slack and other apps now support: here’s how you can support that flow in a Phoenix app.