Wydawca: Packt Publishing
Remote Usability Testing. Actionable insights in user behavior across geographies and time zones
Inge De Bleecker, Rebecca Okoroji
Usability testing is a subdiscipline of User Experience. Its goal is to ensure that a given product is easy to use and the user's experience with the product is intuitive and satisfying. Usability studies are conducted with study participants who are representative of the target users to gather feedback on a user interface. The feedback is then used to refine and improve the user interface.Remote studies involve fewer logistics, allow participation regardless of location and are quicker and cheaper to execute compared to in person studies, while delivering valuable insights. The users are not inhibited by being in a new environment under observation; they can act naturally in their familiar environment. Remote unmoderated studies additionally have the advantage of being independent of time zones.This book will teach you how to conduct qualitative remote usability studies, in particular remote moderated and unmoderated studies. Each chapter provides actionable tips on how to use each methodology and how to compensate for the specific nature of each methodology. The book also provides material to help with planning and executing each study type.
Bryan Feuling
The world of software delivery and deployment has come a long way in the last few decades. From waterfall methods to Agile practices, every company that develops its own software has to overcome various challenges in delivery and deployment to meet customer and market demands. This book will guide you through common industry practices for software delivery and deployment.Throughout the book, you'll follow the journey of a DevOps team that matures their software release process from quarterly deployments to continuous delivery using GitOps. With the help of hands-on tutorials, projects, and self-assessment questions, you'll build your knowledge of GitOps basics, different types of GitOps practices, and how to decide which GitOps practice is the best for your company. As you progress, you'll cover everything from building declarative language files to the pitfalls in performing continuous deployment with GitOps.By the end of this book, you'll be well-versed with the fundamentals of delivery and deployment, the different schools of GitOps, and how to best leverage GitOps in your teams.
Dipti Chhatrapati, Dipti Chhatrapati, Bjoern Rapp
Svetlana Karslioglu
Pachyderm is an open source project that enables data scientists to run reproducible data pipelines and scale them to an enterprise level. This book will teach you how to implement Pachyderm to create collaborative data science workflows and reproduce your ML experiments at scale.You’ll begin your journey by exploring the importance of data reproducibility and comparing different data science platforms. Next, you’ll explore how Pachyderm fits into the picture and its significance, followed by learning how to install Pachyderm locally on your computer or a cloud platform of your choice. You’ll then discover the architectural components and Pachyderm's main pipeline principles and concepts. The book demonstrates how to use Pachyderm components to create your first data pipeline and advances to cover common operations involving data, such as uploading data to and from Pachyderm to create more complex pipelines. Based on what you've learned, you'll develop an end-to-end ML workflow, before trying out the hyperparameter tuning technique and the different supported Pachyderm language clients. Finally, you’ll learn how to use a SaaS version of Pachyderm with Pachyderm Notebooks.By the end of this book, you will learn all aspects of running your data pipelines in Pachyderm and manage them on a day-to-day basis.
Resilient Cybersecurity. Reconstruct your defense strategy in an evolving cyber world
Mark Dunkerley
Building a Comprehensive Cybersecurity Program addresses the current challenges and knowledge gaps in cybersecurity, empowering individuals and organizations to navigate the digital landscape securely and effectively. Readers will gain insights into the current state of the cybersecurity landscape, understanding the evolving threats and the challenges posed by skill shortages in the field.This book emphasizes the importance of prioritizing well-being within the cybersecurity profession, addressing a concern often overlooked in the industry. You will construct a cybersecurity program that encompasses architecture, identity and access management, security operations, vulnerability management, vendor risk management, and cybersecurity awareness. It dives deep into managing Operational Technology (OT) and the Internet of Things (IoT), equipping readers with the knowledge and strategies to secure these critical areas.You will also explore the critical components of governance, risk, and compliance (GRC) within cybersecurity programs, focusing on the oversight and management of these functions. This book provides practical insights, strategies, and knowledge to help organizations build and enhance their cybersecurity programs, ultimately safeguarding against evolving threats in today's digital landscape.
Adnan Masood, Heather Dawe, Ed Price, Dr....
Responsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as bias, fairness, and model governance. Throughout the book, you’ll gain an understanding of FairLearn and InterpretML, along with Google What-If Tool, ML Fairness Gym, IBM AI 360 Fairness tool, and Aequitas. You’ll uncover various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations. You’ll gain practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise setting. Additionally, you’ll explore interpretability toolkits and fairness measures offered by major cloud AI providers like IBM, Amazon, Google, and Microsoft, while discovering how to use FairLearn for fairness assessment and bias mitigation. You’ll also learn to build explainable models using global and local feature summary, local surrogate model, Shapley values, anchors, and counterfactual explanations.By the end of this book, you’ll be well-equipped with tools and techniques to create transparent and accountable machine learning models.