This post will explain deploying Azure Machine Learning models to Azure App Service. This allows for easier model deployments, especially for those users who do not want to deploy to e.g., Azure Kubernetes Service. In this post, we will follow the proposed approach from the official documentation to a certain degree, but we will also […]
This posts shows how to use custom images in Azure Machine Learning for model training. “Why would I need that?” – you might ask – “Doesn’t AML handle all of this?” – and yes, you’re right, Azure Machine Learning does all of that. However, there are a few scenarios where building your own custom images […]
This post shows how to perform a full Azure Machine Learning deployment using Terraform. This includes enterprise features like setting up VNET-bindings and Private Link. You can reuse this sample and adapt it to your own needs. In short, the proposed Terraform deployment will roll out the following environment: This includes: Azure Machine Learning Workspace […]
This post outlines how you can mount a Dataset to a Compute Instance in Azure Machine. This can help exploring file-based datasets in Jupyter, especially for large datasets where download to the disk of the Compute Instance is impractical. Furthermore, this method can also help during exploration phase, where you probably want to read only […]
In last week’s post, we looked into how we can use Automated Machine Learning for building and deploying Models as APIs. This week, we’ll take the same model we have trained, but deploy it to Azure Kubernetes Service (AKS). Machine Learning Model deployment is such a critical task, and using AKS is the production-approved way […]
In this post we’ll look into using Azure Automated Machine Learning for deploying Machine Learning Models as APIs into production. As an example, we will be training and deploying a simple text sentiment analysis service, using the IMDB reviews dataset (subsampled to 1000 examples). We will achieve this by building the following architecture: In detail, we […]
Updated – August 2019: Deployment will now work properly even when including additional pip packages in the Function App! In this post, we’re looking at deploying Azure Functions in Python by using Azure DevOps for Continuous Integration or Continuous Deployment. We’ve already covered how to write Azure Functions in Python in this last post, so […]
Anomaly detection on time-series data is a crucial component of many modern systems like predictive maintenance, security applications or sales performance monitoring. It allows to detect events, that look suspicions or fall outside the distribution of the majority of the data points. This post explains how to perform anomaly detection on time-series data with Azure. […]
In this post, we’re looking at writing Azure Functions in Python and walk through the most common usage patterns. Over the past months, I’ve used Azure Functions in nearly every project I’ve been working on. They have come a long way in terms of feature set and language support, and overall offer a great way […]
Over the past year at Microsoft, I worked with a lot of partners and customers to build their customer-facing chatbots on Azure using the Bot Framework. The Bot Framework is Microsoft’s open source framework for building chat- and speech-enabled bots and virtual assistants. This post points out the key differences between Microsoft Bot Framework v3 […]