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Data manipulation and analysis are far easier than you might imagine—in fact, using tools that come standard with your desktop computer, you can learn how to extract, manipulate, and analyze data (and metadata) of any size and complexity.
Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions Understand the generative AI lifecycle, its core technologies, and implementation risks Book DescriptionDavid Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.What you will learn Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture Gain an understanding of AI risk management frameworks and techniques Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using artificial intelligence services and custom models Dive into generative AI with use cases, architecture patterns, and RAG Who this book is for This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.
When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links
"Like all organizations, libraries are generating more data than ever before and are keen to use it. Data manipulation and analysis is far easier than most people imagine. This book demystifies the process of working with data, familiarizing readers with a small number of simple tools, and easily digestible but powerful concepts. Using tools that come with desktop computers, readers will learn to extract, manipulate, and analyze data (and metadata) of any size and complexity. Kyle Banerjee, experienced author of in data and digital library topics, is determined to take the fear out of the command line. This book will be useful to librarians developing their skills, introducing concepts and tools gradually. Starter topics, most of which can be accomplished with a single-word command, will include: -how to use the output of one program as input for another -redirecting the results of that to any file or program -sorting files of any size by any criteria -identifying duplicates - listing the number of occurrences for each entry As readers develop a firm grasp of the fundamentals, they will learn progressively more sophisticated tasks such as comparing files, converting data from one format to another, reformatting values (e.g. converting inconsistent dates to a consistent format), combining data from multiple files, and communicating with APIs (Application Programming Interfaces) built into their systems. Each chapter with more examples that power users might appreciate, but others can skip over without impeding their ability to understand anything else in the book. Table of Contents 1. Introduction 2. Getting started 3. Directing output - making programs and files work with each other 4. Regular expressions -- the Swiss Army knife of data 5. Understanding data formats Model, namespaces, and validation 6. Application Programming Interfaces (APIs) - talk to programs across the Web 7. Putting it all together 8. More advanced topics 9. One line solutions for common library tasks 10. Command reference 11. Glossary"--
What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis
Wisdom from the best and the brightest in the industry, this visual effects bible belongs on the shelf of anyone working in or aspiring to work in VFX. The book covers techniques and solutions all VFX artists/producers/supervisors need to know, from breaking down a script and initial bidding, to digital character creation and compositing of both live-action and CG elements. In-depth lessons on stereoscopic moviemaking, color management and digital intermediates are included, as well as chapters on interactive games and full animation authored by artists from EA and Dreamworks respectively. From predproduction to acquisition to postproduction, every aspect of the VFX production workflow is given prominent coverage. VFX legends such as John Knoll, Mike Fink, and John Erland provide you with invaluable insight and lessons from the set, equipping you with everything you need to know about the entire visual effects workflow. Simply a must-have book for anyone working in or wanting to work in the VFX industry.
The award-winning VES Handbook of Visual Effects remains the most complete guide to visual effects techniques and best practices available today. This new edition has been updated to include the latest, industry-standard techniques, technologies, and workflows for the ever-evolving fast paced world of visual effects. The Visual Effects Society (VES) tasked the original authors to update their areas of expertise, such as AR/VR Moviemaking, Color Management, Cameras, VFX Editorial, Stereoscopic and the Digital Intermediate, as well as provide detailed chapters on interactive games and full animation. Additionally, 56 contributors share their best methods, tips, tricks, and shortcuts developed through decades of trial and error and real-world, hands-on experience. This third edition has been expanded to feature lessons on 2.5D/3D Compositing; 3D Scanning; Digital Cinematography; Editorial Workflow in Animated and Visual Effects Features; Gaming updates; General Geometry Instancing; Lens Mapping for VFX; Native Stereo; Real-Time VFX and Camera Tracking; Shot/Element Pulls and Delivery to VFX; Techvis; VFX Elements and Stereo; Virtual Production; and VR/AR (Virtual Reality / Augmented Reality). A must-have for anyone working in or aspiring to work in visual effects, The VES Handbook of Visual Effects, Third Edition covers essential techniques and solutions for all VFX artists, producers, and supervisors, from pre-production to digital character creation, compositing of both live-action and CG elements, photorealistic techniques, and much more. With subjects and techniques clearly and definitively presented in beautiful four-color, this handbook is a vital resource for any serious VFX artist.
"Like all organizations, libraries are generating more data than ever before and are keen to use it. Data manipulation and analysis is far easier than most people imagine. This book demystifies the process of working with data, familiarizing readers with a small number of simple tools, and easily digestible but powerful concepts. Using tools that come with desktop computers, readers will learn to extract, manipulate, and analyze data (and metadata) of any size and complexity. Kyle Banerjee, experienced author of in data and digital library topics, is determined to take the fear out of the command line. This book will be useful to librarians developing their skills, introducing concepts and tools gradually. Starter topics, most of which can be accomplished with a single-word command, will include: -how to use the output of one program as input for another -redirecting the results of that to any file or program -sorting files of any size by any criteria -identifying duplicates - listing the number of occurrences for each entry As readers develop a firm grasp of the fundamentals, they will learn progressively more sophisticated tasks such as comparing files, converting data from one format to another, reformatting values (e.g. converting inconsistent dates to a consistent format), combining data from multiple files, and communicating with APIs (Application Programming Interfaces) built into their systems. Each chapter with more examples that power users might appreciate, but others can skip over without impeding their ability to understand anything else in the book. Table of Contents 1. Introduction 2. Getting started 3. Directing output - making programs and files work with each other 4. Regular expressions -- the Swiss Army knife of data 5. Understanding data formats Model, namespaces, and validation 6. Application Programming Interfaces (APIs) - talk to programs across the Web 7. Putting it all together 8. More advanced topics 9. One line solutions for common library tasks 10. Command reference 11. Glossary"--
Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies.
The Screen Combat Handbook is an essential guide to navigating the unique challenges of putting combat on screen. Explore the process from the early stages of preproduction planning all the way through to editing and sound design, and everything in-between. This book uses practical instruction, examples, interviews, and illustrations to show how to plan, shoot, and assemble safe and effective fight sequences. It includes sections on thoughtful and practical design choices in set, wardrobe, props, and effects, preproduction planning, on-set protocol, fight choreography and coordination, shot planning and technical tricks, acting choices, effective cinematography, and impactful editing and sound design. It provides an invaluable resource for all those involved including directors, fight coordinators, actors and stunt players, and any filmmaker attempting to shoot an exciting action scene safely. Whether working on a no-budget indie production or on a professional set, this is your ultimate guide to screen combat and fight choreography.