Over the course of this chapter, we have deep dived into what goes into the ONNX format and what it offers to the community. In addition, we also created a brand new detection engine using the pre-trained Tiny YOLO model in ML.NET.
And with that, this concludes your deep dive into ML.NET. Between the first page of this book and this one, you have hopefully grown to understand the power that ML.NET offers in a very straightforward feature-rich abstraction. With ML.NET constantly evolving (much like .NET), there will be no doubt about the evolution of ML.NET's feature sets and deployment targets, ranging from embedded Internet of Things (IoT) devices to mobile devices. I hope this book was beneficial for your deep dive into ML.NET and machine learning. In addition, I hope that as you approach problems in the future, you will first think about whether the problem would benefit from utilizing ML.NET to solve the problem more efficiently and, potentially, better overall. Given...
