SQL Server Advanced Troubleshooting and Performance Tuning
This practical book provides a comprehensive overview of troubleshooting and performance tuning best practices for Microsoft SQL Server. Database engineers, including database developers and administrators, will learn how to identify performance issues, troubleshoot the system in a holistic fashion, and properly prioritize tuning efforts to attain the best system performance possible. Author Dmitri Korotkevitch, Microsoft Data Platform MVP and Microsoft Certified Master (MCM), explains the interdependencies between SQL Server database components. You'll learn how to quickly diagnose your system and discover the root cause of any issue. Techniques in this book are compatible with all versions of SQL Server and cover both on-premises and cloud-based SQL Server installations. Discover how performance issues present themselves in SQL Server; Learn about SQL Server diagnostic tools, methods, and technologies; Perform health checks on SQL Server installations; Learn the dependencies be ...
This practical book provides a comprehensive overview of troubleshooting and performance tuning best practices for Microsoft SQL Server. Database engineers, including database developers and administrators, will learn how to identify performance issues, troubleshoot the system in a holistic fashion, and properly prioritize tuning efforts to attain the best system performance possible. Author Dmitri Korotkevitch, Microsoft Data Platform MVP and Microsoft Certified Master (MCM), explains the interdependencies between SQL Server database components. You'll learn how to quickly diagnose your system and discover the root cause of any issue. Techniques in this book are compatible with all versions of SQL Server and cover both on-premises and cloud-based SQL Server installations. Discover how performance issues present themselves in SQL Server; Learn about SQL Server diagnostic tools, methods, and technologies; Perform health checks on SQL Server installations; Learn the dependencies be ...
Certified Kubernetes Administrator (CKA) Study Guide
The ability to administer and monitor a Kubernetes cluster is in high demand today. To meet this need, the Cloud Native Computing Foundation developed a certification exam to establish an administrator's credibility and value in the job market to confidently work in a Kubernetes environment. The Certified Kubernetes Administrator (CKA) certification exam is different from the typical multiple-choice format of other professional certifications. Instead, the CKA is a performance-based exam that requires deep knowledge of the tasks under immense time pressure. This study guide walks you through all the topics covered to fully prepare you for the exam. Author Benjamin Muschko also shares his personal experience with preparing for all aspects of the exam. Learn when and how to apply Kubernetes concepts to administer and troubleshoot a production-grade cluster; Understand the objectives, abilities, and tips and tricks needed to pass the CKA exam; Explore the ins and outs of the kube ...
The ability to administer and monitor a Kubernetes cluster is in high demand today. To meet this need, the Cloud Native Computing Foundation developed a certification exam to establish an administrator's credibility and value in the job market to confidently work in a Kubernetes environment. The Certified Kubernetes Administrator (CKA) certification exam is different from the typical multiple-choice format of other professional certifications. Instead, the CKA is a performance-based exam that requires deep knowledge of the tasks under immense time pressure. This study guide walks you through all the topics covered to fully prepare you for the exam. Author Benjamin Muschko also shares his personal experience with preparing for all aspects of the exam. Learn when and how to apply Kubernetes concepts to administer and troubleshoot a production-grade cluster; Understand the objectives, abilities, and tips and tricks needed to pass the CKA exam; Explore the ins and outs of the kube ...
Software Architecture Metrics
Software architecture metrics are key to the maintainability and architectural quality of a software project and they can warn you about dangerous accumulations of architectural and technical debt early in the process. In this practical book, leading hands-on software architects share case studies to introduce metrics that every software architect should know. This isn't a book about theory. It's more about practice and implementation, about what has already been tried and worked. Detecting software architectural issues early is crucial for the success of your software: it helps mitigate the risk of poor performance and lowers the cost of repairing those issues. Written by practitioners for software architects and software developers eager to explore successful case studies, this guide will help you learn more about decision and measurement effectiveness. Through contributions from 10 prominent practitioners, this book shares key software architecture metrics to help you set the ...
Software architecture metrics are key to the maintainability and architectural quality of a software project and they can warn you about dangerous accumulations of architectural and technical debt early in the process. In this practical book, leading hands-on software architects share case studies to introduce metrics that every software architect should know. This isn't a book about theory. It's more about practice and implementation, about what has already been tried and worked. Detecting software architectural issues early is crucial for the success of your software: it helps mitigate the risk of poor performance and lowers the cost of repairing those issues. Written by practitioners for software architects and software developers eager to explore successful case studies, this guide will help you learn more about decision and measurement effectiveness. Through contributions from 10 prominent practitioners, this book shares key software architecture metrics to help you set the ...
Essential Math for Data Science
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning; Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon; Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance; Manipulate vectors and matrices and perform matrix decomposition; Integrate and build upon incremental ...
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning; Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon; Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance; Manipulate vectors and matrices and perform matrix decomposition; Integrate and build upon incremental ...
Practical Simulations for Machine Learning
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine; Use a game engine to synthesize images for use as training data; Create simulation environments designed for training deep reinforcement learning and imitation learning models; Us ...
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine; Use a game engine to synthesize images for use as training data; Create simulation environments designed for training deep reinforcement learning and imitation learning models; Us ...
Consul: Up and Running
With the advent of microservices, Kubernetes, public cloud, and hybrid computing, site reliability and DevOps engineers are facing more complexity than ever before. Service mesh is an exciting new technology that promises to help tackle this complexity. A service mesh provides you with a unified control plane to manage application networking across these distinct platforms. With this definitive guide, you'll learn how to automate networking for simple and secure application delivery with Consul. Author Luke Kysow, Consul engineer at HashiCorp, demonstrates how this service mesh solution provides a software-driven approach to security, observability, reliability, and traffic management. Once you learn how to deploy Consul on multiple platforms, you'll be able to take control of application traffic, prevent outages, view metrics, integrate with legacy systems, and more. Dive into the characteristics of service meshes, zero trust networking, and traffic-shaping patterns; Deploy C ...
With the advent of microservices, Kubernetes, public cloud, and hybrid computing, site reliability and DevOps engineers are facing more complexity than ever before. Service mesh is an exciting new technology that promises to help tackle this complexity. A service mesh provides you with a unified control plane to manage application networking across these distinct platforms. With this definitive guide, you'll learn how to automate networking for simple and secure application delivery with Consul. Author Luke Kysow, Consul engineer at HashiCorp, demonstrates how this service mesh solution provides a software-driven approach to security, observability, reliability, and traffic management. Once you learn how to deploy Consul on multiple platforms, you'll be able to take control of application traffic, prevent outages, view metrics, integrate with legacy systems, and more. Dive into the characteristics of service meshes, zero trust networking, and traffic-shaping patterns; Deploy C ...
Introduction to Python for Computational Science and Engineering
This book summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms. The later chapters touch upon numerical libraries such as numpy and scipy each of which deserves much more space than provided here. We aim to enable the reader to learn independently how to use other functionality of these libraries using the available documentation (online and through the packages itself). ...
This book summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms. The later chapters touch upon numerical libraries such as numpy and scipy each of which deserves much more space than provided here. We aim to enable the reader to learn independently how to use other functionality of these libraries using the available documentation (online and through the packages itself). ...
Confessions of an IT Manager
From IT projects that have trampled all over the guiding principles of successful development, to tips on training your manager to the required standard, each of Phil's tales is shot through with a wild seam of anarchic humour. And yet, in each case, the humor is just a sugar coating for a pill of sage advice, wrought from years of rough-and-tumble in the IT industry. In his more reflective moments, he gives vent to his inner poet, penning romantic elegies to the SQL Server index, or quietly condemning recruitment agencies to Dante's inferno for crimes against innocent contractors. Phil Factor is a legend in his own runtime. Scurrilous, absurd, confessional and scathing by turns, Confessions of an IT Manager targets the idiocy, incompetence and overreach of the IT management industry from vantage point all the way up and down the greasy pole. ...
From IT projects that have trampled all over the guiding principles of successful development, to tips on training your manager to the required standard, each of Phil's tales is shot through with a wild seam of anarchic humour. And yet, in each case, the humor is just a sugar coating for a pill of sage advice, wrought from years of rough-and-tumble in the IT industry. In his more reflective moments, he gives vent to his inner poet, penning romantic elegies to the SQL Server index, or quietly condemning recruitment agencies to Dante's inferno for crimes against innocent contractors. Phil Factor is a legend in his own runtime. Scurrilous, absurd, confessional and scathing by turns, Confessions of an IT Manager targets the idiocy, incompetence and overreach of the IT management industry from vantage point all the way up and down the greasy pole. ...
Automated Machine Learning in Action
Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It's written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you're new to ML, you'll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning. Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. ...
Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It's written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you're new to ML, you'll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning. Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. ...
Inside Deep Learning
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...