Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence Key Features Get to grips with core machine learning algorithms along with AWS implementation Build model training and inference pipelines and deploy machine learning models to the Amazon Web Services (AWS) cloud Learn all about the AWS services available for machine learning in order to pass the MLS-C01 exam Book DescriptionThe AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML. What you will learn Understand all four domains covered in the exam, along with types of questions, exam duration, and scoring Become well-versed with machine learning terminologies, methodologies, frameworks, and the different AWS services for machine learning Get to grips with data preparation and using AWS services for batch and real-time data processing Explore the built-in machine learning algorithms in AWS and build and deploy your own models Evaluate machine learning models and tune hyperparameters Deploy machine learning models with the AWS infrastructure Who this book is forThis AWS book is for professionals and students who want to prepare for and pass the AWS Certified Machine Learning Specialty exam or gain deeper knowledge of machine learning with a special focus on AWS. Beginner-level knowledge of machine learning and AWS services is necessary before getting started with this book.

Design, secure, and protect the privacy of edge analytics applications using platforms and tools such as Microsoft's Azure IoT Edge, MicroPython, and Open Source Computer Vision (OpenCV) Key Features Become well-versed with best practices for implementing automated analytical computations Discover real-world examples to extend cloud intelligence Develop your skills by understanding edge analytics and applying it to research activities Book DescriptionEdge analytics has gained attention as the IoT model for connected devices rises in popularity. This guide will give you insights into edge analytics as a data analysis model, and help you understand why it's gaining momentum. You'll begin with the key concepts and components used in an edge analytics app. Moving ahead, you'll delve into communication protocols to understand how sensors send their data to computers or microcontrollers. Next, the book will demonstrate how to design modern edge analytics apps that take advantage of the processing power of modern single-board computers and microcontrollers. Later, you'll explore Microsoft Azure IoT Edge, MicroPython, and the OpenCV visual recognition library. As you progress, you'll cover techniques for processing AI functionalities from the server side to the sensory side of IoT. You'll even get hands-on with designing a smart doorbell system using the technologies you've learned. To remove vulnerabilities in the overall edge analytics architecture, you'll discover ways to overcome security and privacy challenges. Finally, you'll use tools to audit and perform real-time monitoring of incoming data and generate alerts for the infrastructure. By the end of this book, you'll have learned how to use edge analytics programming techniques and be able to implement automated analytical computations. What you will learn Discover the key concepts and architectures used with edge analytics Understand how to use long-distance communication protocols for edge analytics Deploy Microsoft Azure IoT Edge to a Raspberry Pi Create Node-RED dashboards with MQTT and Text to Speech (TTS) Use MicroPython for developing edge analytics apps Explore various machine learning techniques and discover how machine learning is related to edge analytics Use camera and vision recognition algorithms on the sensory side to design an edge analytics app Monitor and audit edge analytics apps Who this book is forIf you are a data analyst, data architect, or data scientist who is interested in learning and practicing advanced automated analytical computations, then this book is for you. You will also find this book useful if you're looking to learn edge analytics from scratch. Basic knowledge of data analytics concepts is assumed to get the most out of this book.

Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable enterprise-level deployment Key Features Build scalable, seamless, and enterprise-ready cloud-based machine learning applications using TensorFlow Enterprise Discover how to accelerate the machine learning development life cycle using enterprise-grade services Manage Google's cloud services to scale and optimize AI models in production Book DescriptionTensorFlow as a machine learning (ML) library has matured into a production-ready ecosystem. This beginner's book uses practical examples to enable you to build and deploy TensorFlow models using optimal settings that ensure long-term support without having to worry about library deprecation or being left behind when it comes to bug fixes or workarounds. The book begins by showing you how to refine your TensorFlow project and set it up for enterprise-level deployment. You'll then learn how to choose a future-proof version of TensorFlow. As you advance, you'll find out how to build and deploy models in a robust and stable environment by following recommended practices made available in TensorFlow Enterprise. This book also teaches you how to manage your services better and enhance the performance and reliability of your artificial intelligence (AI) applications. You'll discover how to use various enterprise-ready services to accelerate your ML and AI workflows on Google Cloud Platform (GCP). Finally, you'll scale your ML models and handle heavy workloads across CPUs, GPUs, and Cloud TPUs. By the end of this TensorFlow book, you'll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment. What you will learn Discover how to set up a GCP TensorFlow Enterprise cloud instance and environment Handle and format raw data that can be consumed by the TensorFlow model training process Develop ML models and leverage prebuilt models using the TensorFlow Enterprise API Use distributed training strategies and implement hyperparameter tuning to scale and improve your model training experiments Scale the training process by using GPU and TPU clusters Adopt the latest model optimization techniques and deployment methodologies to improve model efficiency Who this book is forThis book is for data scientists, machine learning developers or engineers, and cloud practitioners who want to learn and implement various services and features offered by TensorFlow Enterprise from scratch. Basic knowledge of the machine learning development process will be useful.

Get to grips with security assessment, vulnerability exploitation, workload security, and encryption with this guide to ethical hacking and learn to secure your AWS environment Key Features Perform cybersecurity events such as red or blue team activities and functional testing Gain an overview and understanding of AWS penetration testing and security Make the most of your AWS cloud infrastructure by learning about AWS fundamentals and exploring pentesting best practices Book DescriptionCloud security has always been treated as the highest priority by AWS while designing a robust cloud infrastructure. AWS has now extended its support to allow users and security experts to perform penetration tests on its environment. This has not only revealed a number of loopholes and brought vulnerable points in their existing system to the fore, but has also opened up opportunities for organizations to build a secure cloud environment. This book teaches you how to perform penetration tests in a controlled AWS environment. You'll begin by performing security assessments of major AWS resources such as Amazon EC2 instances, Amazon S3, Amazon API Gateway, and AWS Lambda. Throughout the course of this book, you'll also learn about specific tests such as exploiting applications, testing permissions flaws, and discovering weak policies. Moving on, you'll discover how to establish private-cloud access through backdoor Lambda functions. As you advance, you'll explore the no-go areas where users can't make changes due to vendor restrictions and find out how you can avoid being flagged to AWS in these cases. Finally, this book will take you through tips and tricks for securing your cloud environment in a professional way. By the end of this penetration testing book, you'll have become well-versed in a variety of ethical hacking techniques for securing your AWS environment against modern cyber threats. What you will learn Set up your AWS account and get well-versed in various pentesting services Delve into a variety of cloud pentesting tools and methodologies Discover how to exploit vulnerabilities in both AWS and applications Understand the legality of pentesting and learn how to stay in scope Explore cloud pentesting best practices, tips, and tricks Become competent at using tools such as Kali Linux, Metasploit, and Nmap Get to grips with post-exploitation procedures and find out how to write pentesting reports Who this book is forIf you are a network engineer, system administrator, or system operator looking to secure your AWS environment against external cyberattacks, then this book is for you. Ethical hackers, penetration testers, and security consultants who want to enhance their cloud security skills will also find this book useful. No prior experience in penetration testing is required; however, some understanding of cloud computing or AWS cloud is recommended.

Gain hands-on experience working with the architecture, implementation, deployment, and data migration of Dynamics 365 Customer Engagement Key Features Explore different tools to evaluate, implement, and proactively maintain Dynamics 365 for CE Integrate Dynamics 365 CE with applications such as Power BI, PowerApps, and Microsoft Power Automate Design application architecture, explore deployment choices, and perform data migration Book DescriptionMicrosoft Dynamics 365 for Customer Engagement (CE) is one of the leading customer relationship management (CRM) solutions that help companies to effectively communicate with their customers and allows them to transform their marketing strategies. Complete with detailed explanations of the essential concepts and practical examples, this book will guide you through the entire life cycle of implementing Dynamics 365 CE for your organization or clients, and will help you avoid common pitfalls while increasing efficiency at every stage of the project. Starting with the foundational concepts, the book will gradually introduce you to Microsoft Dynamics 365 features, plans, and products. You'll learn various implementation strategies and requirement gathering techniques, and then design the application architecture by converting your requirements into technical and functional designs. As you advance, you'll learn how to configure your CRM system to meet your organizational needs, customize Dynamics 365 CE, and extend its capabilities by writing client-side and server-side code. Finally, you'll integrate Dynamics 365 CE with other applications and explore its business intelligence capabilities. By the end of this Microsoft Dynamics 365 book, you'll have gained an in-depth understanding of all the key components necessary for successful Dynamics 365 CE implementation. What you will learn Explore the new features of Microsoft Dynamics 365 CE Understand various project management methodologies, such as Agile, Waterfall, and DevOps Customize Dynamics 365 CE to meet your business requirements Integrate Dynamics 365 with other applications, such as PowerApps, Power Automate, and Power BI Convert client requirements into functional designs Extend Dynamics 365 functionality using web resources, custom logic, and client-side and server-side code Discover different techniques for writing and executing test cases Understand various data migration options to import data from legacy systems Who this book is forThis book is for consultants, project managers, administrators, and solution architects who want to set up Microsoft Dynamics 365 Customer Engagement in their business. Although not necessary, basic knowledge of Dynamics 365 will help you get the most out of this book.

Harness the power of Finance and Operations apps, and discover all you need for their implementation Key Features Manage and plan different Dynamics configurations, designs, and products Learn how to manage projects for pre-sales and implementation using Microsoft Dynamics Lifecycle Services (LCS) Discover various integration planning techniques, tools, and frameworks such as PowerApps and Power Automate Book DescriptionMicrosoft Dynamics 365 for Finance and Operations is a modern cloud ERP platform that adopts a mobile-first approach suitable for medium-to-large enterprises. This book covers the entire implementation process of Dynamics 365 Finance and Operation Apps, including post-implementation and business transformation. The updated second edition starts with an introduction to Microsoft Dynamics 365, describing different apps and tools under it. You will learn about different implementation methodologies such as Waterfall and Agile, for your projects. We will cover various application components and architectures of Dynamics such as requirements processing, development, reports and analytics, and integration. With the help of tips, techniques, and best practices, you'll explore strategies for managing configurations and data migrations. As you read further, you'll discover development tools and processes in Dynamics for building customized solutions in Dynamics. The book will also demonstrate analytics and financial reporting options such as Power BI and Cortana Intelligence. Finally, you'll learn the importance of testing and explore various automated testing strategies. By the end of this book, you will have gained the necessary knowledge to implement Microsoft business solutions with Dynamics 365 for Finance and Operations Apps. What you will learn Understand the architecture of Dynamics 365 for Finance and Operations Apps Implement Dynamics with confidence to manage finances in your business Get up to speed with different methodologies and support cycles of the Microsoft Dynamics architecture Explore best practices to analyze the requirements of your business Understand the technique of data migration from legacy systems Leverage the capabilities of Power BI to make informed business decisions Manage all your upgrades through One Version service updates Who this book is forThis book is for consultants, technical managers, project managers, or solution architects who are looking to implement Microsoft Dynamics 365 Finance and Operations apps in their business. A basic understanding of the enterprise resource planning (ERP) implementation process and software lifecycle is expected.

Build a strong foundation in SAS data warehousing by understanding data transformation code and policy, data stewardship and management, interconnectivity between SAS and other warehousing products, and print and web reporting Key Features Understand how to use SAS macros for standardizing extract, transform, and load (ETL) protocols Develop and use data curation files for effective warehouse management Learn how to develop and manage ETL, policies, and print and web reports that meet user needs Book DescriptionSAS is used for various functions in the development and maintenance of data warehouses, thanks to its reputation of being able to handle 'big data'. This book will help you learn the pros and cons of storing data in SAS. As you progress, you'll understand how to document and design extract-transform-load (ETL) protocols for SAS processes. Later, you'll focus on how the use of SAS arrays and macros can help standardize ETL. The book will also help you examine approaches for serving up data using SAS and explore how connecting SAS to other systems can enhance the data warehouse user's experience. By the end of this data management book, you will have a fundamental understanding of the roles SAS can play in a warehouse environment, and be able to choose wisely when designing your data warehousing processes involving SAS. What you will learn Develop efficient ways to manage data input/output (I/O) in SAS Create and manage extract, transform, and load (ETL) code in SAS Standardize ETL through macro variables, macros, and arrays Identify data warehouse users and ensure their needs are met Design crosswalk and other variables to serve analyst needs Maintain data curation files to improve communication and management Use the output delivery system (ODS) for print and web reporting Connect other products to SAS to optimize storage and reporting Who this book is forThis book is for data architects, managers leading data projects, and programmers or developers using SAS who want to effectively maintain a data lake, data mart, or data warehouse.