Engineering Skills for Data Scientists

MLOps: Building Continuous Training and Delivery Pipelines

MLOps is an emerging engineering movement aimed at accelerating the delivery of reliable, working ML software on an ongoing basis. This post provides an intro to MLOps and gives you an example project to get you started with building your own ML pipelines using GitHub Actions and Google Cloud.

Engineering Skills for Data Scientists

Serverless ML: Deploying Lightweight Models at Scale

Deploying ML models 'into production' as scalable APIs can be tricky. This post looks at how Serverless Functions can make deployment easier for some applications, and gives an example project to get you started deploying your own models as Google Cloud Functions.

Engineering Skills for Data Scientists

A Brief Introduction to Serverless Computing

This post introduces the concepts behind 'serverless computing' -- a way of quickly and easily deploying lightweight apps (e.g. APIs). It looks at the associated advantages and disadvantages of serverless, and gives a short example showing how to deploy your own serverless function to Google Cloud.

Engineering Skills for Data Scientists

Fire: Simple CLIs done right

Creating CLIs can help improve accessibility and reuse of your scripts and packages, but they can also be a bit of a pain to set up and maintain. Fire makes building CLIs for your latest ML pipeline a breeze.