Introducing the AWS ML stack

The AWS ML services and features are organized into three layers of the stack, keeping in mind that some developers and data scientists are expert ML practitioners who are comfortable working with ML frameworks, algorithms, and infrastructure to build, train, and deploy models.

For these experts, the bottom layer of the AWS ML stack offers powerful CPU and GPU compute instances (the https://aws.amazon.com/ec2/instance-types/p4/ instances offer the highest performance for ML training in the cloud today), support for major ML frameworks including TensorFlow, PyTorch, and MXNet, which customers can use to build models with Amazon SageMaker as a managed experience, or using deep learning AMIs and containers on Amazon EC2 instances.

You can see the three layers of the AWS ML stack in the next figure. For more details, please refer to https://aws.amazon.com/machine-learning/infrastructure/:

To make ML more accessible and expansive, at the middle layer of the stack, Amazon SageMaker is a fully managed ML platform that removes the undifferentiated heavy lifting at each step of the ML process. Launched in 2018, SageMaker is one of the fastest-growing services in AWS history and is built on Amazon's two decades of experience in building real-world ML applications. With SageMaker Studio, developers and data scientists have the first fully integrated development environment designed specifically for ML. To learn how to build ML models using Amazon SageMaker, refer to Julien Simon's book, Learn Amazon SageMaker, also published by Packt (https://www.packtpub.com/product/learn-amazon-sagemaker/9781800208919):

Figure 1.7 – A tabular list of Amazon SageMaker features for each step of the ML workflow

For customers who are not interested in dealing with models and training, at the top layer of the stack, the AWS AI services provide pre-trained models with easy integration by means of API endpoints for common ML use cases including speech, text, vision, recommendations, and anomaly detection:

Figure 1.8 – AWS AI services

Alright, it's time that we started getting technical. Now that we understand how cloud computing played a major role in bringing ML and AI to the mainstream and how adding NLP to your application can accelerate business outcomes, let's deep dive into the NLP services Amazon Textract for document analysis and Amazon Comprehend for advanced text analytics.

Ready? Let's go!!