Chapter 1: Fundamentals of an MLOps Workflow
Machine learning (ML) is maturing from research to applied business solutions. However, the grim reality is that only 2% of companies using ML have successfully deployed a model in production to enhance their business processes, reported by DeepLearning.AI (https://info.deeplearning.ai/the-batch-companies-slipping-on-ai-goals-self-training-for-better-vision-muppets-and-models-china-vs-us-only-the-best-examples-proliferating-patents). What makes it so hard? And what do we need to do to improve the situation?
To get a solid understanding of this problem and its solution, in this chapter, we will delve into the evolution and intersection of software development and ML. We'll begin by reflecting on some of the trends in traditional software development, starting from the waterfall model to agile to DevOps practices, and how these are evolving to industrialize ML-centric applications. You will be introduced to a systematic approach to operationalizing AI using Machine Learning Operations (MLOps). By the end of this chapter, you will have a solid understanding of MLOps and you will be equipped to implement a generic MLOps workflow that can be used to build, deploy, and monitor a wide range of ML applications.
In this chapter, we're going to cover the following main topics:
- The evolution of infrastructure and software development
- Traditional software development challenges
- Trends of ML adoption in software development
- Understanding MLOps
- Concepts and workflow of MLOps
