Programowanie w chmurze

Learning VMware vCloud Air

Yohan Wadia

This book is intended for cloud engineers or administrators who wish to explore and gain hands-on experience of VMware vCloud Air. To make the most of this book, it would be beneficial to have a bit of familiarity with basic VMware vCloud concepts, but no prior experience is required.

Limitless Analytics with Azure Synapse. An end-to-end analytics service for data processing, management, and ingestion for BI and ML

Prashant Kumar Mishra, Mukesh Kumar

Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform.The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features.By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.

LLM Engineer's Handbook. Master the art of engineering large language models from concept to production

Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza...

Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.

Low-Code Application Development with Appian. The practitioner's guide to high-speed business automation at enterprise scale using Appian

Stefan Helzle

This book is an exhaustive overview of how the Appian Low-Code BPM Suite enables tech-savvy professionals to rapidly automate business processes across their organization, integrating people, software bots, and data. This is crucial as 80% of all software development is expected to be carried out in low code by 2024.This practical guide helps you master business application development with Appian as a beginner low-code developer. You'll learn to automate business processes using Appian low-code, records, processes, and expressions quickly and on an enterprise scale. In a fictional development project, guided by step-by-step explanations of the concepts and practical examples, this book will empower you to transform complex business processes into software.At first, you’ll learn the power of no-code with Appian Quick Apps to solve some of your most crucial business challenges. You’ll then get to grips with the building blocks of an Appian, starting with no-code and advancing to low-code, eventually transforming complex business requirements into a working enterprise-ready application.By the end of this book, you'll be able to deploy Appian Quick Apps in minutes and successfully transform a complex business process into low-code process models, data, and UIs to deploy full-featured, enterprise-ready, process-driven, mobile-enabled apps.

Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads

Georgia Kalyva, George Kavvalakis

With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture.By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.

Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

Joshua Arvin Lat

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.

Managing Microsoft Hybrid Clouds. Benefit from hybrid cloud scenarios through this detailed guide to Microsoft Azure Infrastructure Services (IaaS)

Marcel van den Berg

Managing Virtual Infrastructure with Veeam ONE. Discover ways to manage your virtual infrastructure with Veeam ONE in a development environment

Kevin Sapp