Deploying a Machine Learning (ML) model as a live service to be consumed by a business-critical system or directly by end-users can be a scary prospect. This post looks at how you can perform load testing on your model APIs to ensure they can stand up to even the highest-demand situations.
Data models and data formats are an easily overlooked but critical aspect of modern data infrastructure and development work. This post gives an introduction to HDF5 and how to get started using it in Go.
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.
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.
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.
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.