Sztuczna inteligencja

Machine Learning Engineering on AWS. Build, deploy, and operationalize LLMs, AI agents, and generative AI systems on AWS - Second Edition

Joshua Arvin Lat

Modern AI systems increasingly leverage large language models, retrieval-augmented generation, and AI agents to power generative AI applications in the cloud. As organizations operationalize these systems at scale, there is a growing need for engineers with strong machine learning engineering expertise. To stay ahead in this rapidly evolving field, you need a deep understanding of AI and ML concepts as well as, practical, hands-on experience with the platforms and tools used to build and operate production-grade AI systems.Machine Learning Engineering on AWS is a practical guide that shows you how to use AWS services such as Amazon Bedrock and Amazon SageMaker AI to fine-tune, evaluate, and deploy LLMs and generative AI systems. You'll learn how to develop RAG-powered systems, build and deploy AI agents using Bedrock AgentCore and Strands Agents, evaluate models using LLM-as-a-judge techniques, and automate LLMOps pipelines using SageMaker Pipelines. The book also covers best practices for building scalable, secure, and production-ready GenAI systems.AWS AI hero Joshua Arvin Lat equips you with the skills and practical knowledge to handle a wide variety of ML engineering requirements, helping you design, operationalize, and secure generative AI systems and AI agents on AWS with confidence.*Email sign-up and proof of purchase required

Machine Learning for Streaming Data with Python. Rapidly build practical online machine learning solutions using River and other top key frameworks

Joos Korstanje

Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data.You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights.By the end of this book, you will have gained the confidence you need to stream data in your machine learning models.

Machine Learning for Trading. Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems - Third Edition

Stefan Jansen

The rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory.

Machine learning i natural language processing w programowaniu. Podręcznik z ćwiczeniami w Pythonie

Piotr Wróblewski

Wejdź na nowy poziom programowania z ML i NLP Sztuczna inteligencja stale się rozwija. Właściwie codziennie słyszymy o jej rosnących możliwościach, nowych osiągnięciach i przyszłości, jaką nam przyniesie. Jednak w tej książce skupiamy się nie na przyszłości, a na teraźniejszości i praktycznym obliczu AI - na usługach, które świadczy już dziś. Większość najciekawszych zastosowań sztucznej inteligencji bazuje na ML (uczenie maszynowe, ang. machine learning), NLP (przetwarzanie języka naturalnego, ang. natural language processing) i architekturze RAG (ang. retrieval augmented generation) zwiększającej możliwości tzw. dużych modeli językowych (LLM, ang. large language model). Stanowią one podwaliny budowy systemów AI, bez których te systemy często wcale nie mogłyby powstać. Do niedawna ML i NLP pozostawały domeną badaczy i specjalistów - znajdowały się poza zasięgiem praktyków programowania. Aktualnie jest inaczej, szybkie komputery, pojemne pamięci RAM i zaawansowane procesory pozwalają stosować te technologie w codziennej pracy programisty. Szczególnie programisty języka Python, do którego są one niemal "naturalnie" przypisane. Mało tego, od kodujących w Pythonie coraz częściej wręcz wymaga się umiejętności znajomości obszaru AI. Tym bardziej warto sięgnąć po ten podręcznik z ćwiczeniami, dzięki któremu między innymi: Dowiesz się, jak używać Pythona do rozwiązywania problemów AI Poznasz tajniki analizy tekstów, analizy sentymentu Zrozumiesz, jak skutecznie używać algorytmów klasyfikacji, regresji i grupowania do rozwiązywania problemów biznesowych Pokonwersujesz z ChatGPT - i to bez wchodzenia na stronę internetową tego serwisu

Machine Learning Security Principles. Keep data, networks, users, and applications safe from prying eyes

John Paul Mueller

Businesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning.As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references.The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies.This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks.By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.

Managing AI Risk. A practical approach to responsibly managing AI with ISO 42001

IT Governance Publishing, Andrew Pattison

This book is a comprehensive guide to understanding and managing the risks associated with artificial intelligence (AI) technologies. It begins by exploring the fundamental aspects of AI, from its ethical and philosophical dimensions to its impact on organizational risk.As the book progresses, it delves into the creation of a risk-aware AI strategy, integrating AI risk management into existing organizational frameworks. It offers practical insights on identifying and assessing various risks, including data privacy, algorithmic transparency, and operational challenges related to AI deployment and maintenance.This book is designed for professionals, risk managers, and AI practitioners who seek to navigate the complexities of AI in organizations. Whether you’re just beginning to explore AI risks or looking to refine your existing strategies, this book equips you with the tools needed for effective management.By the end of this book, you will have gained a solid understanding of AI-related risks, be able to implement effective AI risk management frameworks, and integrate AI strategies within your organization.

Managing Risks in Digital Transformation. Navigate the modern landscape of digital threats with the help of real-world examples and use cases

Ashish Kumar, Shashank Kumar, Abbas Kudrati

With the rapid pace of digital change today, especially since the pandemic sped up digital transformation and technologies, it has become more important than ever to be aware of the unknown risks and the landscape of digital threats. This book highlights various risks and shows how business-as-usual operations carried out by unaware or targeted workers can lead your organization to a regulatory or business risk, which can impact your organization’s reputation and balance sheet.This book is your guide to identifying the topmost risks relevant to your business with a clear roadmap of when to start the risk mitigation process and what your next steps should be. With a focus on the new and emerging risks that remote-working companies are experiencing across diverse industries, you’ll learn how to manage risks by taking advantage of zero trust network architecture and the steps to be taken when smart devices are compromised. Toward the end, you’ll explore various types of AI-powered machines and be ready to make your business future-proof.In a nutshell, this book will direct you on how to identify and mitigate risks that the ever- advancing digital technology has unleashed.

Marketing przyszłości. Co zrobić, żeby sztuczna inteligencja była dla Ciebie zawodowym wsparciem, a nie zagrożeniem

Kinga Sroka-Gieparda

Marketing w dobie sztucznej inteligencji Rewolucja AI, której jesteśmy świadkami, najprawdopodobniej odmieni nasz świat. Wpłynie także a może przede wszystkim na pracę. Pewne zawody znikną, inne znacznie się zmienią, pojawią się też zupełnie nowe stanowiska. Już dziś szeroko dyskutuje się na przykład o tym, że sztuczna inteligencja odbierze zajęcie marketerom. Czy naprawdę jest się czego bać? A może zamiast obawiać się rozwoju technologicznego, warto mu się przyjrzeć bliżej, poznać go i nauczyć się korzystać z nowych narzędzi po to, by wykonywać swoje zadania łatwiej, szybciej i skuteczniej? Celem, jaki postawiła sobie autorka tej książki, jest wprowadzenie czytelnika w zagadnienia rozwoju i historii AI, a także zapoznanie go z jej narzędziami: od modeli konwersacyjnych i służących do tworzenia treści po modele generujące kod i kreujące obrazy. Poradnik Kingi Sroki-Giepardy jest przeznaczony przede wszystkim dla osób działających w branżach marketingowej i technologicznej, jednak zawarta w nim wiedza przyda się każdemu, kto chce się zorientować, jak wyglądają bieżące możliwości narzędzi AI, i nauczyć się ich używać. Książka w mediach: annawolodko_mintconcept Instagram Magiczna.ja Sylwia Bookstagram Mamao.pl Instagram