Part 3 – DLOps

In this part of the book, you will dive into the exciting realm of deploying, monitoring, and governing deep learning models in production, drawing parallels with MLOps and DevOps. This part will provide you with a comprehensive understanding of the essential components required to ensure the success and impact of your deep learning models in production with real-world utilization.

Throughout the chapters in this part, we’ll explore the various aspects of deploying deep learning models in production, touching upon important considerations such as hardware infrastructure, model packaging, and user interfaces. We’ll also delve into the three fundamental pillars of model governance, which are model utilization, model monitoring, and model maintenance. You’ll learn about the concept of drift and its impact on the performance of deployed deep learning models over time, as well as strategies to handle drift effectively. We’ll also discuss...