Whether you’re looking to acquire a new skill for yourself or to help a young family member with their studies, there is a vast amount of online resources (many completely free) that can help you along the way. For example, Anki flashcards can help you memorise words of a new language; you can learn more about Enlightment and Revolution period and other History on Khan Academy; you could even learn to program a robot to build a castle in your very own virtual world on Minecraft.

This post documents a method to create an end-to-end pipeline which allows us to automatically deploy application to a Kubernetes cluster following a change in the source code. We helped one of our clients, Infotel UK, to set it up, so they can start the delivery of their software in a rapid and streamlined way. The proposed solution is hosted in Amazon Web Services but an equivalent setup should be possible in all major cloud providers including Microsoft Azure and Google Cloud Platform.

In this post, I will show how to deploy a machine learning model using Docker and plumber. This allows users and applications to consume model predictions using a REST API. The final result will be a web server which can be queried to get class predictions for new data, for example using curl curl localhost:8000/getprediction --header "Content-Type: application/json" \ --request POST \ --data @data/example.json We will deploy the toy classification model for the Iris dataset I developed in a previous post.

This blog post aims to introduce the various R packages making up the tidymodels metapackage by classifying Iris flower species from the Iris dataset. The Iris dataset is so famous it has its own Wikipedia Page. It consists of measurements of sepal and petal lengths and widths and the corresponding species name. A traditional machine learning task is to identify the species from the other measurements. The current workflow for a typical classification (or regression) model in tidymodels is: