Chapter 1: Machine Learning and Its Life Cycle in the Cloud
Machine Learning (ML) is a technique that has been around for decades. It is hard to believe how ubiquitous ML is now in our daily life. It has also been a rocky road for the field of ML to become mainstream, until the recent major leap in computer technology. Today's computer hardware is faster, smaller, and smarter. Internet speeds are faster and more convenient. Storage is cheaper and smaller. Now, it is rather easy to collect, store, and process massive amounts of data with the technology we have now. We are able to create sizeable datasets that we were not able to before, train ML models using compute resources that were not available before, and make use of ML models in every corner of our lives.
For example, media streaming companies can now build ML recommendation engines at a global scale using their title collections and customer activity data on their websites to provide the most relevant content in real time in order to optimize the customer experience. The size of the data for both the titles and customer preferences and activity is on a scale that wasn't possible 20 years ago, considering how many of us are currently using a streaming service.
Training an ML model at this scale, using ML algorithms that are becoming increasingly more complex, requires a robust and scalable solution. After a model is trained, companies are able to serve the model at a global scale where millions of users visit the application from web and mobile devices at the same time.
Companies are also creating more and more models for each segment of customers or even one model for one customer. There is another dimension to this – companies are rolling out new models at a pace that would not have been possible to manage without a pipeline that trains, evaluates, tests, and deploys a new model automatically. Cloud computing has provided a perfect foundation for the streaming service provider to perform these ML activities to increase customer satisfaction.
If ML is something that interests you, or if you are already working in the field of ML in any capacity, this book is the right place for you. You will be learning all things ML, and how to build, train, host, and manage ML models in the cloud with actual use cases and datasets along with me throughout the book. I assume you come to this book with a good understanding of ML and cloud computing. The purpose of this first chapter is to set the level of the concepts and terminology of the two technologies, to define the ML life cycle that is going to be the core of this book, and to provide a crash course on Amazon Web Services and its core services, which will be mentioned throughout the book.
In this chapter, we will cover the following:
- Understanding ML and its life cycle
- Building ML in the cloud
- Exploring AWS essentials for ML
- Setting up AWS environment
