3 Tips for Working with Azure ML Compute Instances
My top 3 tips for working better, faster, and just a bit stronger with Azure ML Compute Instances
Azure Machine Learning
Azure ML Managed Online Endpoints - Quickstart
A quickstart guide to deploying machine learning models in production using Azure Machine Learning’s managed online endpoints
How to run Stable Diffusion Web UI on Azure ML Compute Instances
A guide to creating GPU compute instances on Azure ML, installing Stable Diffusion, and running AUTOMATIC1111’s Web UI.
Continuous Deployment for Azure ML Pipelines with Azure DevOps
Because life’s too short to deploy things manually
3 Ways to Pass Data Between Azure ML Pipeline Steps
The issue with machine learning pipelines is that they need to pass state from one step to another. When this works, it’s a beautiful thing to behold. When it doesn’t, well, it’s not pretty, and I think the clip below sums this up pretty well. made a Rube Goldberg machine pic.twitter.com/gWRNnmm5Ic — COLiN BURGESS (@Colinoscopy) April 30, 2020 Azure ML Pipelines are no stranger to this need for passing data between steps, so you have a variety of options at your disposal....
Reverse Engineering an Azure AutoML Forecasting Model
How to create a model based on an Azure AutoML-trained baseline, using standard open-source components where possible and adapting AutoML specific code where needed
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
[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....