Over the course of this chapter, we have deep-dived into what goes into production-ready model training from the original purpose question to a trained model. Through this deep dive, we have examined the level of effort that is needed to create detailed features through production thought processes and feature engineering. We then reviewed the challenges, the ways to address the training, and how to test dataset questions. Lastly, we also dove into the importance of an actual model building pipeline, using an entirely automated process.
In the next chapter, we will utilize a pre-built TensorFlow model in a WPF application to determine if a submitted image contains certain objects or not. This deep dive will explore how ML.NET provides an easy-to-use interface for TensorFlow models.
