Take a look at our Aws books. Shulph carries a great selection of Aws books, and we are always adding more.
Develop technical skills and expertise to automate AWS networking tasks Key Features A fast paced guide that will help you pass the exam with confidence Learn advanced skill sets to build effective AWS networking solutions Enhance your AWS skills with practice exercises and mock tests Book Description Amazon has recently come up a with specialty certifications which validates a particular user's expertise that he/she would want to build a career in. Since the Cloud market now demands of AWS networking skills this becomes the most wanted certification to upheld ones industry portfolio. This book would be your ideal companion to getting skilled with complex and creative networking solutions. Cloud practitioners or associate-level certified individuals interested in validating advanced skills in networking can opt for this practical guide. This book will include topics that will help you design and implement AWS and hybrid IT network architectures along with some network automation tasks. You will also delve deep into topics that will help you design and maintain network architecture for all AWS services. Like most of our certification guides this book will also follow a unique approach of testing your learning with chapter-level practice exercises and certification-based mock tests. The exam mock tests will help you gauge whether you are ready to take the certification exam or not. This book will also be an advanced guide for networking professionals to enhance their networking skills and get certified. By the end of this book, you will be all equipped with AWS networking concepts and techniques and will have mastered core architectural best practices. What you will learn Formulate solution plans and provide guidance on AWS architecture best practices Design and deploy scalable, highly available, and fault-tolerant systems on AWS Identify the tools required to replicate an on-premises network in AWS Analyze the access and egress of data to and from AWS Select the appropriate AWS service based on data, compute, database, or security requirements Estimate AWS costs and identify cost control mechanisms Who this book is for If you are a system administrator, or a network engineer interested in getting certified with an advanced Cloud networking certification then this book is for you. Prior experience in Cloud administration and networking would be necessary.
Scale and maintain outstanding performance in your AWS-based infrastructure using DevOps principles Key Features Implement continuous integration and continuous deployment pipelines on AWS Gain insight from an expert who has worked with Silicon Valley's most high-profile companies Implement DevOps principles to take full advantage of the AWS stack and services Book Description The DevOps movement has transformed the way modern tech companies work. Amazon Web Services (AWS), which has been at the forefront of the cloud computing revolution, has also been a key contributor to the DevOps movement, creating a huge range of managed services that help you implement DevOps principles. Effective DevOps with AWS, Second Edition will help you to understand how the most successful tech start-ups launch and scale their services on AWS, and will teach you how you can do the same. This book explains how to treat infrastructure as code, meaning you can bring resources online and offline as easily as you control your software. You will also build a continuous integration and continuous deployment pipeline to keep your app up to date. Once you have gotten to grips will all this, we'll move on to how to scale your applications to offer maximum performance to users even when traffic spikes, by using the latest technologies, such as containers. In addition to this, you'll get insights into monitoring and alerting, so you can make sure your users have the best experience when using your service. In the concluding chapters, we'll cover inbuilt AWS tools such as CodeDeploy and CloudFormation, which are used by many AWS administrators to perform DevOps. By the end of this book, you'll have learned how to ensure the security of your platform and data, using the latest and most prominent AWS tools. What you will learn Implement automatic AWS instance provisioning using CloudFormation Deploy your application on a provisioned infrastructure with Ansible Manage infrastructure using Terraform Build and deploy a CI/CD pipeline with Automated Testing on AWS Understand the container journey for a CI/CD pipeline using AWS ECS Monitor and secure your AWS environment Who this book is for Effective DevOps with AWS is for you if you are a developer, DevOps engineer, or you work in a team which wants to build and use AWS for software infrastructure. Basic computer science knowledge is required to get the most out of this book.
Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to solve real-world problems. You'll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You'll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you'll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you'll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
Create highly efficient design patterns for scalability, redundancy, and high availability in the AWS Cloud Key Features Build highly robust systems using the cloud infrastructure Make web applications resilient against scheduled and accidental downtime Explore and apply Amazon-provided services in unique ways to solve common design problems Book Description Whether you're just getting your feet wet in cloud infrastructure or already creating complex systems, this book will guide you through using the patterns to fit your system needs. Starting with patterns that cover basic processes such as source control and infrastructure-as-code, the book goes on to introduce cloud security practices. You'll then cover patterns of availability and scalability and get acquainted with the ephemeral nature of cloud environments. You'll also explore advanced DevOps patterns in operations and maintenance, before focusing on virtualization patterns such as containerization and serverless computing. In the final leg of your journey, this book will delve into data persistence and visualization patterns. You'll get to grips with architectures for processing static and dynamic data, as well as practices for managing streaming data. By the end of this book, you will be able to design applications that are tolerant of underlying hardware failures, resilient against an unexpected influx of data, and easy to manage and replicate. What you will learn Implement scaling policies on schedules, influxes in traffic, and deep health checks Make complete use of highly available and redundant storage Design content delivery networks to improve user experience Optimize databases through caching and sharding Apply patterns to solve common problems Implement repeatable processes for deploying systems Who this book is for If you're an architect, solution provider, or DevOps community member looking to implement repeatable patterns for deploying and maintaining services in the Amazon cloud infrastructure, this book is for you. You'll need prior experience of using AWS understand key concepts covered in the book, as it focuses on the patterns rather than the basics of using AWS.
Get up and running with AWS automation using CloudFormation Key Features Explore the fundamentals of AWS CloudFormation Get acquainted with concepts such as CloudFormation templates and mappings Learn to implement Infrastructure as a Code (IaC) on AWS Book Description As the Amazon Web Services (AWS) infrastructure is gradually moving towards cloud, managing cloud-related tasks efficiently continues to be a challenge for system administrators. CloudFormation is a language developed for managing infrastructure-related services efficiently on AWS and its features help secure the AWS resource deployment process. Learn CloudFormation serves as a fundamental guide to kick-start your journey on CloudFormation. We will introduce you to the basic concepts on IaC and the AWS services required for implementing automation and infrastructure management. Then, we deep dive into concepts such as CloudFormation mapping, conditions, limit, and output and EC2. In the concluding chapters, you will manage the entire AWS infrastructure using CloudFormation templates. By the end of this book, you will get up and running with IaC with CloudFormation. What you will learn Understand AWS CloudFormation Develop AWS CloudFormation templates Deploy AWS CloudFormation for AWS resources Build your first AWS CloudFormation project Explore AWS Security features Deploy testing and production stages using CloudFormation Who this book is for Learn CloudFormation is for cloud engineers, system administrators, cloud architects, or any stakeholders working in the field of cloud development or cloud administration. Basic knowledge of AWS is necessary.
Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlow Learn model optimization, and understand how to scale your models using simple and secure APIs Develop, train, tune and deploy neural network models to accelerate model performance in the cloud Book Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you'll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learn Manage AI workflows by using AWS cloud to deploy services that feed smart data products Use SageMaker services to create recommendation models Scale model training and deployment using Apache Spark on EMR Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker Build deep learning models on AWS using TensorFlow and deploy them as services Enhance your apps by combining Apache Spark and Amazon SageMaker Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.