Engineering

Technical design documents are detailed game plans for features or solutions to technical problems. They end up saving your team a lot of time and code.

A machine learning (ML) project requires collaboration across many roles. Here are high level steps in the end-to-end ML lifecycle for each role they play.

A process to create, review, and merge pull requests is a must when working as a team. Communication and collaboration effectively gets the job done.

Structure your interview process to emulate what it’s like to work together on your team and a typical day on the job.

We raised money. You might be thinking “so what, everyone else did as well”. I’ll try not to make this another fundraising announcement.

Having a continuous integration/continuous delivery pipeline greatly speeds up the process of testing, building, and deploying without sacrificing quality.

Without good performance, machine learning (ML) models won’t provide much value. Introducing, strategies to improve model performance metrics and runtime.

When working with AI, it’s important to know how to import data sets, read through tables, and understand what the structure is.

Light Bulb is a tool to help you label, train, test and deploy machine learning models without any coding.

Don’t let your role define your responsibilities. Do things you’re unqualified for. Go out of your way to serve others.