Monday, 3 February 2020

Pushing NuGet Packages to Azure DevOps Artifact Feeds Manually

In the build pipelines of Azure DevOps we can easily push a NuGet package, using a NuGet push step and selecting the artifact feed, in the Azure DevOps organization or team project. But you may sometimes need to push packages manually to Azure Artifact feeds. Let’s look at how we can do that.

Friday, 17 January 2020

Running EF Commands in Builds with .NET Core 3.1 in Hosted Agents

When you run builds with Entity Framework (EF) commands such as dotnet ef migrations script with .NET Core 2.2 it would work without any issue. However, if you upgrade your projects to use .NET Core 3.1 your build may fail with issue below., when executing dotnet ef migrations script, to generate a script out of your EF migrations.
error NETSDK1045: The current .NET SDK does not support targeting .NET Core 3.1. Either target .NET Core 2.2 or lower, or use a version of the .NET SDK that supports .NET Core 3.1.

Thursday, 9 January 2020

Deploying Machine Learning (ML) Model with Azure Pipeline Using Deployable Artifact from Build

We have discussed how to create a Machine Learning (ML) model as a deployable artifact in the post “Training Machine Learning (ML) Model with Azure Pipeline and Output ML Model as Deployable Artifact” which is based on the open source ML repo, (https://github.com/SaschaDittmann/MLOps-Lab.git) with data by Sascha Dittmann, which also contains the code to train a model.

Thursday, 2 January 2020

Training Machine Learning (ML) Model with Azure Pipeline and Output ML Model as Deployable Artifact

Training a machine learning model requires wide range of quality data to get the ML model trained in such a way that it can provide accurate predictions. Azure build pipeline can run the python tests written to validate the data quality and the train a model with uploaded data to Azure ML workspace. In this post on “Setup MLOPS workspace using Azure DevOps pipeline” it is clearly explained how to setup an Azure ML workspace in a new resource group dynamically with Azure CLI ML command extension. The post “Setup MLOPS workspace using Azure DevOps pipeline” as well as this post on training a model with Azure pipelines use the open source ML repo (https://github.com/SaschaDittmann/MLOps-Lab.git) with data by Sascha Dittmann, and code to train a model.

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