Book Image

Machine Learning Engineering on AWS

By : Joshua Arvin Lat
Book Image

Machine Learning Engineering on AWS

By: Joshua Arvin Lat

Overview of this book

There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you’ll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You’ll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements.
Table of Contents (19 chapters)
1
Part 1: Getting Started with Machine Learning Engineering on AWS
5
Part 2:Solving Data Engineering and Analysis Requirements
8
Part 3: Diving Deeper with Relevant Model Training and Deployment Solutions
11
Part 4:Securing, Monitoring, and Managing Machine Learning Systems and Environments
14
Part 5:Designing and Building End-to-end MLOps Pipelines

Using AWS Deep Learning Containers to train an ML model

At this point, you might be wondering what makes a deep learning model different from other ML models. Deep learning models are networks of interconnected nodes that communicate with each other, similar to how networks of neurons communicate in a human brain. These models make use of multiple layers in the network, similar to what we have in the following diagram. Having more layers and more neurons per layer gives deep learning models the ability to process and learn complex non-linear patterns and relationships:

Figure 3.5 – Deep learning model

Deep learning has several practical applications in natural language processing (NLP), computer vision, and fraud detection. In addition to these, here are some of its other applications and examples as well:

  • Generative Adversarial Networks (GANs): These can be used to generate realistic examples from the original dataset, similar to what we had...