Genomics in the AWS Cloud


Book Description

Perform genome analysis and sequencing of data with Amazon Web Services Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web Services enables a person who has moderate familiarity with AWS Cloud to perform full genome analysis and research. Using the information in this book, you'll be able to take a FASTQ file containing raw data from a lab or a BAM file from a service provider and perform genome analysis on it. You'll also be able to identify potentially pathogenic gene sequences. Get an introduction to Whole Genome Sequencing (WGS) Make sense of WGS on AWS Master AWS services for genome analysis Some key advantages of using AWS for genomic analysis is to help researchers utilize a wide choice of compute services that can process diverse datasets in analysis pipelines. Genomic sequencers that generate raw data files are located in labs on premises and AWS provides solutions to make it easy for customers to transfer these files to AWS reliably and securely. Storing Genomics and Medical (e.g., imaging) data at different stages requires enormous storage in a cost-effective manner. Amazon Simple Storage Service (Amazon S3), Amazon Glacier, and Amazon Elastics Block Store (Amazon EBS) provide the necessary solutions to securely store, manage, and scale genomic file storage. Moreover, the storage services can interface with various compute services from AWS to process these files. Whether you're just getting started or have already been analyzing genomics data using the AWS Cloud, this book provides you with the information you need in order to use AWS services and features in the ways that will make the most sense for your genomic research.




Genomics in the Cloud


Book Description

Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytesâ??or over 50 million gigabytesâ??of genomic data, and theyâ??re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian Oâ??Connor of the UC Santa Cruz Genomics Institute, guide you through the process. Youâ??ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra




AWS Cloud Engineer Guide


Book Description

DESCRIPTION Cloud computing provides a more efficient, reliable, secure, and cost-effective way to run applications. Cloud computing offers customers access to rapidly growing amounts of data storage and computation resources while centralizing IT operations in the cloud provider's datacenter or in colocation data centers. Understand AWS basics such as EC2, VPCs, S3, and IAM while learning to design secure and scalable cloud architectures. This book guides you through automating infrastructure with CloudFormation and exploring advanced topics like containers, continuous integration and continuous delivery (CI/CD) pipelines, and cloud migration. You will also discover serverless computing with Lambda, API Gateway, and DynamoDB, enabling you to build efficient, modern applications. With real-world examples and best practices, this resource helps you optimize your AWS environment for both performance and cost, ensuring you can build and maintain robust cloud solutions. By the end of this book, you will be able to confidently design, build, and operate scalable and secure cloud solutions on AWS. Gain the expertise to leverage the full potential of cloud computing and drive innovation in your organization. KEY FEATURES ● Learn about AWS cloud in-depth with real-world examples and scenarios. ● Expand your understanding of serverless and containerization compute technology on AWS. ● Explore API’s along with API Gateway and its different use cases. WHAT YOU WILL LEARN ● How to get started with and launch EC2 instances. ● Working with and simplifying VPC’s, security groups, and network access control lists on AWS. ● Learn how to secure your AWS environment through the use of IAM roles and policies. ● Learn how to build scalable and fault-tolerant database systems using AWS database services such as RDS and Aurora. ● Learn how to set up a CI/CD pipeline on AWS. WHO THIS BOOK IS FOR Whether you are a system administrator, cloud architect, solutions architect, cloud engineer, DevOps engineer, security engineer, or cloud professional, this book provides valuable insights and practical guidance to help you build and operate robust cloud solutions on AWS. TABLE OF CONTENTS 1. Creating an AWS Environment 2. Amazon Elastic Compute Cloud 3. Amazon Virtual Private Cloud 4. Amazon S3: Simple Storage Service 5. Amazon API Gateway 6. AWS Database Services 7. Elastic Load Balancing and Auto Scaling 8. Amazon Route 53 9. Decouple Applications 10. CloudFormation 11. AWS Monitoring 12. AWS Security and Encryption 13. AWS Containers 14. Automating Deployments with CI/CD in AWS 15. AWS Cloud Migrations




AWS Cloud Automation


Book Description

How to automate AWS Cloud using Terraform IaC best practices KEY FEATURES ● Learn how to create and deploy AWS Cloud Resources using Terraform IaC. ● Manage large and complex AWS infrastructures. ● Manage diverse storage options like S3 and EBS for optimal performance and cost-efficiency. DESCRIPTION AWS Cloud Automation allows organizations to effortlessly organize and handle their cloud resources. Terraform, an open-source provisioning tool, transforms the old manual way of doing things by allowing users to define, deploy, and maintain infrastructure as code, ensuring consistency, scalability, and efficiency. This book explains AWS Cloud Automation using Terraform, which is a simple and clear syntax that lets users define the infrastructure needs. Terraform simplifies setting up and managing infrastructure, reducing errors and fostering team collaboration. It enables version control, letting you monitor changes and implement CI/CD pipelines, effortlessly. The book guides you in creating and managing AWS resources through a simple configuration file, allowing you to define virtual machines, networks, databases, and more. Discover how Terraform makes organizing infrastructure code easy, promoting reusability and simple maintenance with consistent patterns across projects and teams. This book will empower readers of AWS Cloud Automation to embrace a modern, scalable, and efficient approach to managing cloud infrastructure. By combining the power of Terraform with the flexibility of AWS. WHAT YOU WILL LEARN ● Implement automated workflows with Terraform in CI/CD pipelines, for consistent and reliable deployments. ● Secure your cloud environment with robust Identity and Access Management (IAM) policies. ● Build and deploy highly available and scalable applications using EC2, VPC, and ELB. ● Automate database deployments and backups with RDS and DynamoDB for worry-free data management. ● Implement serverless architectures with EKS and Fargate for agile and cost-effective development. WHO THIS BOOK IS FOR This book is crafted for both aspiring and seasoned infrastructure enthusiasts, cloud architects, solution architects , SysOps Administrators, and DevOps professionals ready to apply the power of Terraform as their AWS go-to Infrastructure as Code (IaC) tool. TABLE OF CONTENTS 1. AWS DevOps and Automation Tools Set 2. AWS Terraform Setup 3. IAM, Governance and Policies Administration 4. Automating AWS Storage Deployment and Configuration 5. VPC and Network Security Tools Automation 6. Automating EC2 Deployment of various Workloads 7. Automating ELB Deployment and Configurations 8. AWS Route53 Policy and Routing Automation 9. AWS EKS and Fargate Deployments 10. Databases and Backup Services Automation 11. Automating and Bootstrapping Monitoring Service




Predictive Computing and Information Security


Book Description

This book describes various methods and recent advances in predictive computing and information security. It highlights various predictive application scenarios to discuss these breakthroughs in real-world settings. Further, it addresses state-of-art techniques and the design, development and innovative use of technologies for enhancing predictive computing and information security. Coverage also includes the frameworks for eTransportation and eHealth, security techniques, and algorithms for predictive computing and information security based on Internet-of-Things and Cloud computing. As such, the book offers a valuable resource for graduate students and researchers interested in exploring predictive modeling techniques and architectures to solve information security, privacy and protection issues in future communication.




Applied Machine Learning for Healthcare and Life Sciences Using AWS


Book Description

Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizations Key FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook Description While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence. What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is for This book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.




Applied Machine Learning and High-Performance Computing on AWS


Book Description

Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.




Web-Based Services: Concepts, Methodologies, Tools, and Applications


Book Description

The recent explosion of digital media, online networking, and e-commerce has generated great new opportunities for those Internet-savvy individuals who see potential in new technologies and can turn those possibilities into reality. It is vital for such forward-thinking innovators to stay abreast of all the latest technologies. Web-Based Services: Concepts, Methodologies, Tools, and Applications provides readers with comprehensive coverage of some of the latest tools and technologies in the digital industry. The chapters in this multi-volume book describe a diverse range of applications and methodologies made possible in a world connected by the global network, providing researchers, computer scientists, web developers, and digital experts with the latest knowledge and developments in Internet technologies.




Cloud Computing


Book Description

In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.




Cloud Technology: Concepts, Methodologies, Tools, and Applications


Book Description

As the Web grows and expands into ever more remote parts of the world, the availability of resources over the Internet increases exponentially. Making use of this widely prevalent tool, organizations and individuals can share and store knowledge like never before. Cloud Technology: Concepts, Methodologies, Tools, and Applications investigates the latest research in the ubiquitous Web, exploring the use of applications and software that make use of the Internet’s anytime, anywhere availability. By bringing together research and ideas from across the globe, this publication will be of use to computer engineers, software developers, and end users in business, education, medicine, and more.