Summary

In this opening chapter, we implemented our first DL model by following the pretrain plus fine-tuning core DL development paradigm using PyTorch lightning-flash for a text sentiment classification model. We learned about the five stages of the full life cycle of DL. We defined the concept of MLOps along with the three foundation layers and four ML/DL pillars, where MLflow plays critical roles in all four pillars (data, model, code, and explainability). Finally, we described the challenges in DL data, model, code, and explainability.

With the knowledge and first DL model experience gained in this chapter, we are now ready to learn about and implement MLflow in our DL model in the following chapters. In the next chapter, we will start with the implementation of a DL model with MLflow autologging enabled.