Chapter 1: Deep Learning Life Cycle and MLOps Challenges
The past few years have seen great success in Deep Learning (DL) for solving practical business, industrial, and scientific problems, particularly for tasks such as Natural Language Processing (NLP), image, video, speech recognition, and conversational understanding. While research in these areas has made giant leaps, bringing these DL models from offline experimentation to production and continuously improving the models to deliver sustainable values is still a challenge. For example, a recent article by VentureBeat (https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/) found that 87% of data science projects never make it to production. While there might be business reasons for such a low production rate, a major contributing factor is the difficulty caused by the lack of experiment management and a mature model production and feedback platform.
This chapter will help us to understand the challenges and bridge these gaps by learning the concepts, steps, and components that are commonly used in the full life cycle of DL model development. Additionally, we will learn about the challenges of an emerging field known as Machine Learning Operations (MLOps), which aims to standardize and automate ML life cycle development, deployment, and operation. Having a solid understanding of these challenges will motivate us to learn the skills presented in the rest of this book using MLflow, an open source, ML full life cycle platform. The business values of adopting MLOps' best practices are numerous; they include faster time-to-market of model-derived product features, lower operating costs, agile A/B testing, and strategic decision making to ultimately improve customer experience. By the end of this chapter, we will have learned about the critical role that MLflow plays in the four pillars of MLOps (that is, data, model, code, and explainability), implemented our first working DL model, and grasped a clear picture of the challenges with data, models, code, and explainability in DL.
In this chapter, we're going to cover the following main topics:
- Understanding the DL life cycle and MLOps challenges
- Understanding DL data challenges
- Understanding DL model challenges
- Understanding DL code challenges
- Understanding DL explainability challenges
