ML has gone to the cloud and developers can now use it as a service. AWS has implemented ML services at different levels of abstraction. ML application services, for example, aim to offer out-of-the-box solutions for specific problem domains. AWS Lex is a very clear example of an ML application as a service, where people can implement chatbots with minimum development.
AWS Rekognition is another example, which aims to identify objects, people, text, scenes, and activities in images and videos. AWS provides many other ML application services, which will be covered in the next chapter of this book.
Apart from application services, AWS also provides ML development platforms, such as SageMaker. Unlike out-of-the-box services such as AWS Lex and Rekognition, SageMaker is a development platform that will let you build, train, and deploy your own models with much more flexibility.
SageMaker speeds up the development and deployment process by automatically handling the necessary infrastructure for the training and inference pipelines of your models. Behind the scenes, SageMaker orchestrates other AWS services (such as EC2 instances, load balancers, auto-scaling, and so on) to create a scalable environment for ML projects. SageMaker is probably the most important service that you should master for the AWS Machine Learning Specialty exam, and it will be covered in detail in a separate section. For now, you should focus on understanding the different approaches that AWS uses to offer ML-related services.
The third option that AWS offers for deploying ML models is the most generic and flexible one: you can deploy ML models by combining different AWS services and managing them individually. This essentially does what SageMaker does for you, building your applications from scratch. For example, you could use EC2 instances, load balancers, auto-scaling, and an API gateway to create an inference pipeline for a particular model. If you prefer, you can also use AWS serverless architecture to deploy your solution, for example, using AWS Lambda functions.
You are now heading toward the end of this chapter, in which you have learned about several important topics regarding the foundations of ML. You started the chapter with a theoretical discussion about AI, ML, and DL, and how this entire field has grown over the past few years due to the advent of big data platforms, cloud providers, and AI applications.
You then moved on to the differences between supervised, unsupervised, and reinforcement learning, highlighting some use cases related to each of them. This is likely to be a topic in the AWS Machine Learning Specialty exam.
You learned that an ML model is built in many different stages and the algorithm itself is just one part of the modeling process. You also learned about the expected behaviors of a good model.
You did a deep dive into data splitting, where you learned about different approaches to train and validate models, and you became aware of the mythic battle between variance and bias. You completed the chapter by getting a sense of ML frameworks and services.
Coming up next, you will learn about AWS application services for ML, such as Amazon Polly, Amazon Rekognition, Amazon Transcribe, and many other AI-related AWS services. But first, look at some sample questions to give you an idea of what you can expect in the exam.
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