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In Part 1 of this mini-series, you saw how OOP concepts can be used to structure and manipulate code. In this part, you'll see how these ideas are formally defined, and look at a couple of more advanced concepts too.
Xanthus is a Deep Learning (DL) library built on top of Tensorflow and uses the Keras API to implement various neural recommendation model architectures. This post benchmarks the models implemented in Xanthus against some popular 'classic' matrix factorisation models.
Streamlit is a minimal, modern data visualization framework that's rapidly becoming the go-to dataapp framework in the Python ecosystem. This post introduces Streamlit, and shows you how to securely and scalably deploy your Streamlit apps with Google App Engine.
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 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.