Programowanie w chmurze
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.
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.
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.
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.
Mastering Amazon EC2. Unravel the complexities of EC2 to build robust and resilient applications
Badri Kesavan
This comprehensive guide demystifies the complex landscape of Amazon EC2, from fundamentals to advanced concepts. You’ll begin by understanding EC2 core components, creating and managing AMIs, selecting the right instance type and size, establishing networking and connectivity, and implementing security best practices. Next, you’ll start building resilient apps on EC2, load balancing, auto scaling, storage options, performance optimization, monitoring, logging, and auditing. You'll also explore advanced concepts like containers, serverless, hybrid/multi-cloud architectures, high performance computing, big data, and disaster recovery strategies. By the end, you'll be equipped with the knowledge and confidence to harness the full potential of Amazon EC2.
Mastering Ansible. Master the ins and outs of advanced operations with Ansible - Second Edition
Jesse Keating
This book provides you with the knowledge you need to understand how Ansible 2.1 works at a fundamental level and leverage its advanced capabilities. You'll learn how to encrypt Ansible content at rest and decrypt data at runtime. You will master the advanced features and capabilities required to tackle the complex automation challenges of today and beyond. You will gain detailed knowledge of Ansible workflows, explore use cases for advanced features, craft well thought out orchestrations, troubleshoot unexpected behaviour, and extend Ansible through customizations. Finally, you will discover the methods used to examine and debug Ansible operations, helping you to understand and resolve issues.By the end of the book, the readers will be able to unlock the true power of the Ansible automation engine and will tackle complex real world actions with ease.