Building and Automating Penetration Testing Labs in the Cloud. Set up cost-effective hacking environments for learning cloud security on AWS, Azure, and GCP

Joshua Arvin Lat

The significant increase in the number of cloud-related threats and issues has led to a surge in the demand for cloud security professionals. This book will help you set up vulnerable-by-design environments in the cloud to minimize the risks involved while learning all about cloud penetration testing and ethical hacking.This step-by-step guide begins by helping you design and build penetration testing labs that mimic modern cloud environments running on AWS, Azure, and Google Cloud Platform (GCP). Next, you’ll find out how to use infrastructure as code (IaC) solutions to manage a variety of lab environments in the cloud. As you advance, you’ll discover how generative AI tools, such as ChatGPT, can be leveraged to accelerate the preparation of IaC templates and configurations. You’ll also learn how to validate vulnerabilities by exploiting misconfigurations and vulnerabilities using various penetration testing tools and techniques. Finally, you’ll explore several practical strategies for managing the complexity, cost, and risks involved when dealing with penetration testing lab environments in the cloud.By the end of this penetration testing book, you’ll be able to design and build cost-effective vulnerable cloud lab environments where you can experiment and practice different types of attacks and penetration testing techniques.

Machine Learning Engineering on AWS. Build, scale, and secure machine learning systems and MLOps pipelines in production

Joshua Arvin Lat

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.

Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Joshua Arvin Lat

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.