Chapter 1: Getting Started with Automated Machine Learning on AWS
If you have ever had the pleasure of successfully driving a production-ready Machine Learning (ML) application to completion or you are currently in the process of developing your first ML project, I am sure that you will agree with me when I say, "This is not an easy task!"
Why do I say that? Well, if we ignore the intricacies involved in gathering the right training data, analyzing and understanding that data, and then building and training the best possible model, I am sure you will agree that the ML process in itself is a complicated task process, time-consuming, and entirely manual, making it extremely difficult to automate. And it is these factors, plus many more, that contribute to ML tasks being difficult to automate.
The primary goal of this chapter is to emphasize these challenges by reviewing a practical example that sets the stage for why automating the ML process is difficult. This chapter will highlight what governing factors should be considered when performing this automation and how leveraging various Amazon Web Services (AWS) capabilities can make the task of driving ML projects into production less daunting and fully automated. By the end of this chapter, we will have established a common foundation for overcoming these challenges through automation.
Therefore, in this chapter, we will cover the following topics:
- Overview of the ML process
- Complexities in the ML process
- An example of the end-to-end ML process
- How AWS can make automating ML development and the deployment process easier
