Daniel Costea

I know there are thousands of projects about building robots, but I’m talking about really simple robots with some moving parts and AI. We’ve seen them programmed with Python, C, Java, but what about .NET? It has been 6 years now since Microsoft launched .NET Core and we got access to new platforms like Linux, Mac or ARM. There are 6 million .NET developers around the globe. Most of them will be more than happy that they don’t have to deal with integration pain anymore when trying to integrate machine learning, deep learning and the IoT functionality.

When using machine learning, the central point is the machine learning model. Whether you train your own machine learning model or you get one to consume in your production code, you have to know some insights about how it was trained, like the label (or target feature), the data models (input and output), and the scenario that was used for training. Along with these details, it’s very important to know the accuracy of your machine learning model. You can have tools like MLOps to take care of these details, but maybe you don’t.

To conclude what we have covered so far, it's clear that when building a model, the trainer selection is not the most difficult part. AutoML is able to suggest a list with the best models, due to the evaluation metrics which accompany every model. What is much more complex (and time-consuming) is the data preparation which, along with the training pipeline, builds a model ready to make predictions.

At the end of the first part of the series we had a preprocessing pipeline ready to load data and concatenate the features selected for the training into one special feature called Features, and a target feature named Label serving as a category where the selected feature classifies.

The purpose of these series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, and that is possible now using Microsoft ML.NET and Jupyter Notebooks. Moreover, you don't have to be a data scientist to do machine learning.