Introducing Machine Learning and the AWS Machine Learning Stack
Applying Machine Learning (ML) technology to solve tangible business problems has become increasingly popular among business and technology leaders. There are a lot of cutting-edge use cases that have utilized ML in a meaningful way and have shown considerable success. For example, computer vision models can allow you to search for what’s in an image by automatically inferring its content, and Natural Language Processing (NLP) models can understand the intent of a conversation and respond automatically while closely mimicking human interactions. As a matter of fact, you may not even know whether the “entity” on the other side of a phone call is an AI bot or a real person!
While AI technology has a lot of potential for success, there is still a limited understanding of this technology. It is usually concentrated in the hands of a few researchers and advanced partitioners who have spent decades in the field. To solve this knowledge parity, a large section of software and information technology firms such as Amazon Web Services (AWS) are committed to creating tools and services that do not require a deep understanding of the underlying ML technology and are still able to achieve positive results. While these tools democratize AI, the conceptual knowledge of AI and ML is critical for its successful application and should not be ignored.
In this chapter, we will get an understanding of ML and how it differs from traditional software. We will get an overview of a typical ML life cycle and also learn about the steps a data scientist needs to perform to deploy an ML model in production. These concepts are fairly generic and can be applied to any domain or organization where ML is utilized.
By the end of this chapter, you will get a good understanding of how AWS helps democratize ML with purpose-built services that are applicable to developers of all skill levels. We will go through the AWS ML stack and go over the different categories of services that will help you understand how the AWS AI/ML services are organized overall. We’ll cover these topics in the following sections:
- What is ML?
- Exploring the ML life cycle
- Introducing ML on AWS
