Load Testing a Machine Learning Model API
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.
Flask in Production: Minimal Web APIs
Flask is a popular 'micro-framework' for building web APIs in Python. However, getting a Flask API 'into production' can be a little tricky for newcomers. This post provides a minimal template project for a Flask API, and gives some tips on how to build out basic production Flask APIs.
Object-Oriented Programming: A Practical Introduction (Part 1)
Whether you're a fan or not, OOP is a valuable tool in your programming toolkit. It's also sometimes a little bewildering for new programmers (and some more experienced ones too). This post provides a (brief) practical introduction to OOP concepts.
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.