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.
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.
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.
Introducing Xanthus
This post introduces Xanthus - a new open source Deep Learning package built on TensorFlow 2.0 for quickly and easily building state of the art recommendation models in Python.