Informatyka
Shivakumar Gopalakrishnan, Gunther Lenz
Microsoft is now one of the most significant contributors to Kubernetes open source projects. Kubernetes helps to create, configure, and manage a cluster of virtual machines that are preconfigured to run containerized applications.This book will be your guide to performing successful container orchestration and deployment of Kubernetes clusters on Azure. You will get started by learning how to deploy and manage highly scalable applications, along with understanding how to set up a production-ready Kubernetes cluster on Azure. As you advance, you will learn how to reduce the complexity and operational overheads of managing a Kubernetes cluster on Azure.By the end of this book, you will not only be capable of deploying and managing Kubernetes clusters on Azure with ease, but also have the knowledge of best practices for working with advanced AKS concepts for complex systems.
Shivakumar Gopalakrishnan, Gunther Lenz
Microsoft is now one of the most significant contributors to Kubernetes open source projects. Kubernetes helps to create, configure, and manage a cluster of virtual machines that are preconfigured to run containerized applications.This book will be your guide to performing successful container orchestration and deployment of Kubernetes clusters on Azure. You will get started by learning how to deploy and manage highly scalable applications, along with understanding how to set up a production-ready Kubernetes cluster on Azure. As you advance, you will learn how to reduce the complexity and operational overheads of managing a Kubernetes cluster on Azure.By the end of this book, you will not only be capable of deploying and managing Kubernetes clusters on Azure with ease, but also have the knowledge of best practices for working with advanced AKS concepts for complex systems.
Frederik Vos
Azure’s market share has increased massively and enterprises are adopting it rapidly, while Linux is a widely-used operating system and has proven to be one of the most popular workloads on Azure. It has thus become crucial for Linux administrators and Microsoft professionals to be well versed with managing Linux workloads in an Azure environment.With this guide, system administrators will be able to deploy, automate, and orchestrate containers in Linux on Azure. The book follows a hands-on approach to help you understand DevOps, monitor Linux workloads on Azure and perform advanced system administration. Complete with systematic explanations of concepts, examples and self-assessment questions, the chapters will give you useful insights into Linux and Azure. You’ll explore some of Linux’s advanced features for managing multiple workloads and learn to deploy virtual machines (VMs) in Azure. Dedicated sections will also guide you with managing and extending Azure VMs’ capabilities and understanding automation and orchestration with Ansible and PowerShell DSC. In later chapters, you’ll cover useful Linux troubleshooting and monitoring techniques that will enable you to maintain your workload on Azure.By the end of this book, you’ll be able to make the most out of Azure’s services to efficiently deploy and manage your Linux workloads.
Enrico Murru
Low-code platforms allow users to focus on business logic to create solutions without getting trapped in programming complexities. Thanks to its powerful features for designing, developing, and deploying apps without having to hand-code, Salesforce is at the forefront of the low-code development revolution. This book will guide you in building creative applications for solving your business problems using the declarative framework provided by Salesforce.You’ll start by learning how to design your business data model with custom objects, fields, formulas, and validation rules, all secured by the Salesforce security model. You’ll then explore tools such as Workflow, Process Builder, Lightning Flow, and Actions that will help you to automate your business processes with ease. This book also shows you how to use Lightning App Builder to build personalized UIs for your Salesforce applications, explains the value of creating community pages for your organization, and teaches you how to customize them with Experience Builder. Finally, you'll work with the sandbox model, deploy your solutions, and deliver an effective release management strategy.By the end of this Salesforce book, you’ll be ready to customize Salesforce CRM to meet your business requirements by creating unique solutions without writing a single line of code.
Stefan Jansen
The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies.This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies.Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.
Soma Halder, Sinan Ozdemir
Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain.The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not.Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems
Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier,...
Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions.This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications.By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Thomas K Abraham, Parashar Shah, Jen Stirrup,...
Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way.The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure.By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
James D. Miller
IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies.By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples.
Burak Kanber
In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications.Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data.By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications.
Ankur Ankan, Abinash Panda
Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.
Sudharsan Ravichandiran
Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.
Dinesh Rajput
Microservices are the latest right way of developing web applications. Microservices architecture has been gaining momentum over the past few years, but once you've started down the microservices path, you need to test and optimize the services. This book focuses on exploring various testing, monitoring, and optimization techniques for microservices.The book starts with the evolution of software architecture style, from monolithic to virtualized, to microservices architecture. Then you will explore methods to deploy microservices and various implementation patterns. With the help of a real-world example, you will understand how external APIs help product developers to focus on core competencies. After that, you will learn testing techniques, such as Unit Testing, Integration Testing, Functional Testing, and Load Testing. Next, you will explore performance testing tools, such as JMeter, and Gatling. Then, we deep dive into monitoring techniques and learn performance benchmarking of the various architectural components. For this, you will explore monitoring tools such as Appdynamics, Dynatrace, AWS CloudWatch, and Nagios. Finally, you will learn to identify, address, and report various performance issues related to microservices.
Dinesh Rajput
Microservices are the latest right way of developing web applications. Microservices architecture has been gaining momentum over the past few years, but once you've started down the microservices path, you need to test and optimize the services. This book focuses on exploring various testing, monitoring, and optimization techniques for microservices.The book starts with the evolution of software architecture style, from monolithic to virtualized, to microservices architecture. Then you will explore methods to deploy microservices and various implementation patterns. With the help of a real-world example, you will understand how external APIs help product developers to focus on core competencies. After that, you will learn testing techniques, such as Unit Testing, Integration Testing, Functional Testing, and Load Testing. Next, you will explore performance testing tools, such as JMeter, and Gatling. Then, we deep dive into monitoring techniques and learn performance benchmarking of the various architectural components. For this, you will explore monitoring tools such as Appdynamics, Dynatrace, AWS CloudWatch, and Nagios. Finally, you will learn to identify, address, and report various performance issues related to microservices.
Dinesh Rajput
Microservices are the latest right way of developing web applications. Microservices architecture has been gaining momentum over the past few years, but once you've started down the microservices path, you need to test and optimize the services. This book focuses on exploring various testing, monitoring, and optimization techniques for microservices.The book starts with the evolution of software architecture style, from monolithic to virtualized, to microservices architecture. Then you will explore methods to deploy microservices and various implementation patterns. With the help of a real-world example, you will understand how external APIs help product developers to focus on core competencies. After that, you will learn testing techniques, such as Unit Testing, Integration Testing, Functional Testing, and Load Testing. Next, you will explore performance testing tools, such as JMeter, and Gatling. Then, we deep dive into monitoring techniques and learn performance benchmarking of the various architectural components. For this, you will explore monitoring tools such as Appdynamics, Dynatrace, AWS CloudWatch, and Nagios. Finally, you will learn to identify, address, and report various performance issues related to microservices.
Matt Cole
C# is a powerful language when it comes to building applications and software architecture using rich libraries and tools such as .NET.This book will harness the strength of C# in developing microservices architectures and applications.This book shows developers how to develop an enterprise-grade, event-driven, asynchronous, message-based microservice framework using C#, .NET, and various open source tools. We will discuss how to send and receive messages, how to design many types of microservice that are truly usable in a corporate environment. We will also dissect each case and explain the code, best practices, pros and cons, and more.Through our journey, we will use many open source tools, and create file monitors, a machine learning microservice, a quantitative financial microservice that can handle bonds and credit default swaps, a deployment microservice to show you how to better manage your deployments, and memory, health status, and other microservices. By the end of this book, you will have a complete microservice ecosystem you can place into production or customize in no time.
Tieme Woldman
This book is for Django developers looking to create optimized and scalable web applications using microservices. With it, you’ll learn the principles of microservices and message/task queues and build your first microservices with Django RESTful APIs (DFR) and RabbitMQ. You’ll also master the fundamentals, dockerize your microservices, and optimize and secure them for production environments. By the end, you'll have the skills to design and develop production-ready Django microservices applications with DFR, Celery/RabbitMQ, Redis, and Django's cache framework.
Tural Suleymani
Keep up with the ever-evolving web development landscape by mastering JavaScript microservices with expert guidance from Tural Suleymani—a full-stack software engineer, architect, software development teacher, Microsoft MVP, and three-time C# Corner MVP. He distills over a decade of experience crafting high-performance, scalable solutions into this guide. He’ll walk you through the fundamentals of microservices, providing a solid foundation in architecture, design principles, and the necessary tools and technologies. From beginners to seasoned developers, this book offers a clear pathway to mastering microservices with JavaScript.With the help of hands-on tasks that simulate real-world scenarios, you’ll learn how to build reliable and scalable microservices. You’ll explore synchronous and asynchronous communication, real-time data streaming, and how to secure and monitor your services. The book’s emphasis on a design-first approach ensures that your microservices are maintainable and future-proof. Detailed case studies from industry experts will enhance your learning experience and provide practical insights into building microservices in production environments.By the end of this book, you'll be ready to create cloud-ready, high-performing microservices using cutting-edge JavaScript frameworks and tools and tackle real-world challenges, ensuring your applications are secure and efficient.
Juan Antonio Medina Iglesias
With Google's inclusion of first-class support for Kotlin in their Androidecosystem, Kotlin's future as a mainstream language is assured. Microservices helpdesign scalable, easy-to-maintain web applications; Kotlin allows us to takeadvantage of modern idioms to simplify our development and create high-qualityservices. With 100% interoperability with the JVM, Kotlin makes working withexisting Java code easier. Well-known Java systems such as Spring, Jackson, andReactor have included Kotlin modules to exploit its language features.This book guides the reader in designing and implementing services, and producingproduction-ready, testable, lean code that's shorter and simpler than a traditionalJava implementation. Reap the benefits of using the reactive paradigm and takeadvantage of non-blocking techniques to take your services to the next level in termsof industry standards. You will consume NoSQL databases reactively to allow youto create high-throughput microservices. Create cloud-native microservices thatcan run on a wide range of cloud providers, and monitor them. You will create Dockercontainers for your microservices and scale them. Finally, you will deploy yourmicroservices in OpenShift Online.