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Empowering Organizations with Power Virtual Agents. A practical guide to building intelligent chatbots with Microsoft Power Platform

Nicolae Tarla

Power Virtual Agents is a set of technologies released under the Power Platform umbrella by Microsoft. It allows non-developers to create solutions to automate customer interactions and provide services using a conversational interface, thus relieving the pressure on front-line staff providing this kind of support.Empowering Organizations with Power Virtual Agents is a guide to building chatbots that can be deployed to handle front desk services without having to write code. The book takes a scenario-based approach to implementing bot services and automation to serve employees in the organization and external customers. You will uncover the features available in Power Virtual Agents for creating bots that can be integrated into an organization’s public site as well as specific web pages. Next, you will understand how to build bots and integrate them within the Teams environment for internal users. As you progress, you will explore complete examples for implementing automated agents (bots) that can be deployed on sites for interacting with external customers.By the end of this Power Virtual Agents chatbot book, you will have implemented several scenarios to serve external client requests for information, created scenarios to help internal users retrieve relevant information, and processed these in an automated conversational manner.

Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python

Matt Benatan, Jochem Gietema, Marian Schneider

Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don’t know.The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more in how we incorporate model predictions within our applications.Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios.By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems.

Essential Guide to LLMOps. Implementing effective strategies for Large Language Models in deployment and continuous improvement

Ryan Doan

The rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications.This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications.By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.

Essential PySpark for Scalable Data Analytics. A beginner's guide to harnessing the power and ease of PySpark 3

Sreeram Nudurupati

Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework.Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas.By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.

Essential Statistics for Non-STEM Data Analysts. Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

Rongpeng Li

Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks.The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You’ll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you’ll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you’ve uncovered the working mechanism of data science algorithms, you’ll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you’ll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning.By the end of this Essential Statistics for Non-STEM Data Analysts book, you’ll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.

Ethereum Projects for Beginners. Build blockchain-based cryptocurrencies, smart contracts, and DApps

Kenny Vaneetvelde

Ethereum enables the development of efficient, smart contracts that contain code. These smart contracts can interact with other smart contracts to make decisions, store data, and send Ether to others.Ethereum Projects for Beginners provides you with a clear introduction to creating cryptocurrencies, smart contracts, and decentralized applications. As you make your way through the book, you’ll get to grips with detailed step-by-step processes to build advanced Ethereum projects. Each project will teach you enough about Ethereum to be productive right away. You will learn how tokenization works, think in a decentralized way, and build blockchain-based distributed computing systems. Towards the end of the book, you will develop interesting Ethereum projects such as creating wallets and secure data sharing.By the end of this book, you will be able to tackle blockchain challenges by implementing end-to-end projects using the full power of the Ethereum blockchain.

Ethereum Smart Contract Development. Build blockchain-based decentralized applications using solidity

Mayukh Mukhopadhyay

Ethereum is a public, blockchain-based distributed computing platform featuring smart contract functionality. This book is your one-stop guide to blockchain and Ethereum smart contract development. We start by introducing you to the basics of blockchain. You'll learn about hash functions, Merkle trees, forking, mining, and much more. Then you'll learn about Ethereum and smart contracts, and we'll cover Ethereum virtual machine (EVM) in detail. Next, you'll get acquainted with DApps and DAOs and see how they work. We'll also delve into the mechanisms of advanced smart contracts, taking a practical approach.You'll also learn how to develop your own cryptocurrency from scratch in order to understand the business behind ICO. Further on, you'll get to know the key concepts of the Solidity programming language, enabling you to build decentralized blockchain-based applications. We'll also look at enterprise use cases, where you'll build a decentralized microblogging site. At the end of this book, we discuss blockchain-as-a-service, the dark web marketplace, and various advanced topics so you can get well versed with the blockchain principles and ecosystem.

EU General Data Protection Regulation (GDPR). An implementation and compliance guide

IT Governance Publishing, IT Governance Privacy Team

This book provides a thorough exploration of the EU General Data Protection Regulation (GDPR). It starts with the core principles of GDPR, explaining its purpose, key concepts, and how it impacts data controllers and processors. The book covers essential features like data subject rights, data processing principles, and privacy compliance frameworks. It also explores the role of the Data Protection Officer (DPO) and the importance of conducting data protection impact assessments (DPIAs).Focusing on practical implementation, the book highlights the need for robust information security measures to meet GDPR standards. It provides actionable advice on best practices, including managing data breaches, ensuring lawful consent, and processing subject access requests. The guide also addresses the complexities of international data transfers in line with GDPR requirements.Finally, the book outlines GDPR enforcement mechanisms, detailing the powers of supervisory authorities and the steps to demonstrate compliance. This resource offers organizations a comprehensive roadmap to align with GDPR, laying the groundwork for effective data protection and compliance.