Computer Science Fundamentals Object-Oriented Programming: A Practical Introduction (Part 2) 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.
Newsletter Review: September 2020 The second newsletter from this blog, covering featured posts as well as some of the most interesting machine learning and software engineering news, articles and papers from around the web.
Data Science How Good is Xanthus? 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.
Technology Deploying Streamlit Apps to GCP 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.
Computer Science Fundamentals 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.
Newsletter Review: August 2020 The very first newsletter from this blog, covering featured posts as well as some of the most interesting machine learning and software engineering posts from around the web.
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.
Artificial Intelligence 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.
Literature The Freedom of the Press It's easy to feel that the present times are 'unprecedented'. Yet sometimes it's clear that the events of today are echoes of problems familiar to those in the past. This post shares an essay written by Orwell in 1945, that feels particularly relevant to the times.
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.
Personal Books of 2019 Here's my annual list of my favourite books from the past year. There's even a business book on here for the first time.
Programming Cython: Lightspeed Python Python is a wonderful language for many applications, but it is not renowned for its speed. This post looks at how you can quickly and easily use Cython to dramatically accelerate your Python code (in some cases).
Computer Science What is Reservoir Computing? What if there was a framework that could be used to exploit the properties of matter for computation? Originally posted on The Conversation.
