When you have created a machine learning model, you will retrain that model when new data is available. But when I recently added a couple of images to the training set of my own ML.NET model, I was faced with the following exception:

System.InvalidOperationException: 'The asynchronous operation has not completed.'

The application did work for weeks, so what has changed? And more importantly, how to fix this situation?

When using a Docker Registry, like hub.docker.com, you will not often want to delete a published version of an image. You cannot know if someone, somewhere in the world is using that specific version.

But when using a repository as part of your CI/CD pipeline, you might have lots of versions that are not used by anyone anymore. So, what if you want to clean the repository automatically?

In this article, I will show how the delete images by tag, using PowerShell and the CNCF Distribution HTTP API V2. This API is implemented by registries, like the Azure Container Registry service.

I use the MSTest Parser task in a Bamboo build plan to add a report of tests that were executed and their outcome. But sometimes you want to skip running tests. For example, you are focusing on other changes in your CI/CD pipeline and want to keep the feedback loop as short as possible.

The problem is, if you do not run any tests, the parser task will fail. There are no configuration settings to influence this behavior or to disable the task conditionally. I could disable the task manually, but that would affect all other branches at the same time.

So, it was time to create a quick work around.

The Custom Vision service is one of the Azure AI Services that is provided by Microsoft. With custom vision, you can train a model with your own image data. The portal provides a Prediction API for the trained model to classify an image you supply.

There can be reasons why you might not want to use the online REST endpoint. For example, when processing images from a video feed, performance, connectivity, security, and costs might be considerations why you could prefer an offline model.

To work with machine learning in .NET, we can use ML.NET, an open source and cross-platform framework. There are several tutorials how to use this framework. But none of them combine the Bitmap class with a downloaded ONNX model from the custom vision portal.

In this article I will walk through the steps to set up an application to classify bitmaps using a custom vision model with ML.NET.

When trying to have an application work with an ONNX model I downloaded from the Microsoft Custom Vision portal, I got the following exception:

System.TypeInitializationException: 'The type initializer for 'Microsoft.ML.OnnxRuntime.NativeMethods' threw an exception.'

EntryPointNotFoundException: Unable to find an entry point named 'OrtGetApiBase' in DLL 'onnxruntime'.

Searching for the error online did not yield any solutions.

After solving the problem, I wanted to share the solution for anyone else running into the same exception.