Using Azure Automated ML to Predict Ethereum Prices (Crypto Prices with ML)
The first in a series of articles about building production machine learning systems in Azure, thinly veiled as an attempt to predict cryptocurrency prices
Deploying a Machine Learning Model with Azure ML Pipelines
Machine learning pipelines are a way to describe your machine learning process as a series of steps such as data extraction and preprocessing, but also training, deploying, and running models. In this article, I’ll show you how you can use Azure ML Pipelines to deploy an already trained model such as this one, and use it to generate batch predictions multiple times a day. But before we do that, let’s understand why pipelines are so important in machine learning. ...
Getting Started with Automated ML in Azure
A step by step introduction to Automated Machine Learning in Azure while gathering data, creating the necessary Azure resources, and automatically training a model
Migrating from Mercurial to Git (and from Bitbucket to GitHub)
A quick and dirty tutorial for migrating from Mercurial to everyone’s favorite distributed version control system, Git
[Talk] Getting Started with Machine Learning Using Azure Machine Learning Studio and Kaggle Competitions
Long title, I know 🤫. It used to be shorter, as some earlier versions of this talk were called ‘Predicting Survivability on the Titanic’, but this time I wanted to experiment a bit and make it real easy for the audience to decide whether or not this would be interesting for them. And so they did. ...
[Talk] Machine Learning in Azure: Service versus Studio
This is a more detailed version of my Boy meets Girl talk, created specially for Microsoft Ignite | The Tour Amsterdam 2019. Whereas Boy meets Girl was mostly focused on how to deploy a trained model using either Azure ML Service or ML Studio, here I wanted to create a more in-depth comparison of the two tools. This is what led me to the concept of having multiple rounds, with the audience voting for their favourite tool (truth be told, I think I just wanted another go at delivering something similar to my TypeScript versus CoffeeScript talk 🤓). ...
[Talk] Boy meets Girl: A Machine Learning Deployment Story
This was a fun talk to write :). Ever since I saw Azure ML Service being announced, I knew I wanted to compare it with ML Studio, a tool with which I had a bit more experience. And so I did. Since 45 minutes is nowhere near enough to compare the two tools (lesson re-learned the hard way while designing Service versus Studio), I decided to only compare their deployment capabilities, given an already trained model. ...