Chapter 1: A Lap around Automated Machine Learning
This chapter will provide an overview of the concepts, tools, and technologies surrounding automated Machine Learning (ML). This introduction hopes to provide both a solid overview for novices and serve as a reference for experienced ML practitioners. We will start by introducing the ML development life cycle while navigating through the product ecosystem and the data science problems it addresses, before looking at feature selection, neural architecture search, and hyperparameter optimization.
It's very plausible that you are reading this book on an e-reader that's connected to a website that recommended this manuscript based on your reading interests. We live in a world today where your digital breadcrumbs give telltale signs of not only your reading interests, but where you like to eat, which friend you like most, where you will shop next, whether you will show up to your next appointment, and who you would vote for. In this age of big data, this raw data becomes information that, in turn, helps build knowledge and insights into so-called wisdom.
Artificial Intelligence (AI) and its underlying implementations of ML and deep learning help us not only find the metaphorical needle in the haystack, but also to see the underlying trends, seasonality, and patterns in these large data streams to make better predictions. In this book, we will cover one of the key emerging technologies in AI and ML; that is, automated ML, or AutoML for short.
In this chapter, we will cover the following topics:
- The ML development life cycle
- Automated ML
- How automated ML works
- Democratization of data science
- Debunking automated ML myths
- Automated ML ecosystem (open source and commercial)
- Automated ML challenges and limitations
Let's get started!
