// Mixpanel implementation The Mentoring Club - Book recommendations for DataScience & Analytics
Storytelling with Data - A Data Visualization Guide for Business Professionals
Cole Nussbaumer Knaflic

Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

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Lean Analytics - Use Data to Build a Better Startup Faster
Alistair Croll, Benjamin Yoskovitz

If you’re involved with a startup, analytics help you find your way to the right product and market before the money runs out. But with a flood of information available, where do you start? This book shows you what to measure, how to analyze it, and how to report it, whether you’re evaluating your business model, testing new features, enticing investors, or reporting progress to advisers. Written by Alistair Croll (founder of Coradiant) and Ben Yoskovitz (co-founder of Year One Labs), Lean Analytics lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Examples and case studies show entrepreneurs and intrapreneneurs (entrepreneurs inside larger organizations) how to identify and measure a startup’s single most important metric, and how to iterate until you get it right. While the authors primarily cover technology startups, their lessons can be applied far beyond the Web. Even traditional businesses are embracing a lean, learn-first approach, as demonstrated by owners of a San Francisco deli that used a temporary "pop-up" method to optimize their menu and operations before launching a permanent restaurant.

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Invisible Women - Data Bias in a World Designed for Men
Caroline Criado Perez

Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives. Celebrated feminist advocate Caroline Criado Perez investigates shocking root cause of gender inequality and research in Invisible Women†‹, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.

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Learning SQL - Master SQL Fundamentals
Alan Beaulieu

Updated for the latest database management systems -- including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 -- this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, Learning SQL, Second Edition, will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: Move quickly through SQL basics and learn several advanced features Use SQL data statements to generate, manipulate, and retrieve data Create database objects, such as tables, indexes, and constraints, using SQL schema statements Learn how data sets interact with queries, and understand the importance of subqueries Convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With Learning SQL, you'll quickly learn how to put the power and flexibility of this language to work.

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Database System Concepts
Henry F. Korth, S. Sudarshan, Abraham Silberschatz, Professor

Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 6th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.

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Superforecasting - The Art and Science of Prediction
Philip Tetlock, Dan Gardner

The international bestseller 'A manual for thinking clearly in an uncertain world. Read it.' Daniel Kahneman, author of Thinking, Fast and Slow _________________________ What if we could improve our ability to predict the future? Everything we do involves forecasts about how the future will unfold. Whether buying a new house or changing job, designing a new product or getting married, our decisions are governed by implicit predictions of how things are likely to turn out. The problem is, we're not very good at it. In a landmark, twenty-year study, Wharton professor Philip Tetlock showed that the average expert was only slightly better at predicting the future than a layperson using random guesswork. Tetlock's latest project – an unprecedented, government-funded forecasting tournament involving over a million individual predictions – has since shown that there are, however, some people with real, demonstrable foresight. These are ordinary people, from former ballroom dancers to retired computer programmers, who have an extraordinary ability to predict the future with a degree of accuracy 60% greater than average. They are superforecasters. In Superforecasting, Tetlock and his co-author Dan Gardner offer a fascinating insight into what we can learn from this elite group. They show the methods used by these superforecasters which enable them to outperform even professional intelligence analysts with access to classified data. And they offer practical advice on how we can all use these methods for our own benefit – whether in business, in international affairs, or in everyday life. _________________________ 'The techniques and habits of mind set out in this book are a gift to anyone who has to think about what the future might bring. In other words, to everyone.' Economist 'A terrific piece of work that deserves to be widely read . . . Highly recommended.' Independent 'The best thing I have read on predictions . . . Superforecasting is an indispensable guide to this indispensable activity.' The Times

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Small Data - The Tiny Clues That Uncover Huge Trends
Martin Lindstrom Company

The New York Times Bestseller named one of the "Most Important Books of 2016" by Inc, and a Forbes 2016 "Must Read Business Book" 'If you love 'Bones' and 'CSI', this book is your kind of candy' Paco Underhill, author of Why We Buy 'Martin's best book to date. A personal, intuitive, powerful way to look at making an impact with your work' Seth Godin, author of Purple Cow Martin Lindstrom, one of Time Magazine's 100 Most Influential People in The World and a modern-day Sherlock Holmes, harnesses the power of "small data" in his quest to discover the next big thing. In an era where many believe Big Data has rendered human perception and observation 'old-school' or passé, Martin Lindstrom shows that mining and matching technological data with up-close psychological insight creates the ultimate snapshot of who we really are and what we really want. He works like a modern-day Sherlock Holmes, accumulating small clues - the progressively weaker handshakes of Millenials, a notable global decrease in the use of facial powder, a change in how younger consumers approach eating ice cream cones - to help solve a stunningly diverse array of challenges. In Switzerland, a stuffed teddy bear in a teenage girl's bedroom helped revolutionise 1,000 stores - spread across twenty countries - for one of Europe's largest fashion retailers. In Dubai, a distinctive bracelet strung with pearls helped Jenny Craig offset its declining membership in the United States and increase loyalty by 159% in only one year. In China, the look of a car dashboard led to the design of the iRobot, or Roomba, floor cleaner - a great success story. SMALL DATA combines armchair travel with forensic psychology in an interlocking series of international clue-gathering detective stories. It shows Lindstrom using his proprietary CLUES Framework - where big data is merely one part of the overall puzzle - to get radically close to consumers and come up with the counter-intuitive insights that have in some cases helped transform entire industries. SMALL DATA presents a rare behind-the-scenes look at what it takes to create global brands, and reveals surprising and counter-intuitive truths about what connects us all as humans.

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Data Driven: Harnessing Data and AI to Reinvent Customer Engagement
Tom Chavez, Chris O’Hara, Vivek Vaidya

Axiom Business Book Award Silver Medalist in Business TechnologyThe indispensable guide to data-powered marketing from the team behind the data management platform that helps fuel Salesforce―the #1 customer relationship management (CRM) company in the worldA tectonic shift in the practice of marketing is underway. Digital technology, social media, and e-commerce have radically changed the way consumers access information, order products, and shop for services. Using the latest technologies―cloud, mobile, social, internet of things (IoT), and artificial intelligence (AI)―we have more data about consumers and their needs, wants, and affinities than ever before. Data Driven will show you how to:●Target and delight your customers with unprecedented accuracy and success●Bring customers closer to your brand and inspire them to engage, purchase, and remain loyal●Capture, organize, and analyze data from every source and activate it across every channel●Create a data-powered marketing strategy that can be customized for any audience●Serve individual consumers with highly personalized interactions●Deliver better customer service for the best customer experience●Improve your products and optimize your operating systems●Use AI and IoT to predict the future direction of marketsYou’ll discover the three principles for building a successful data strategy and the five sources of data-driven power. You’ll see how top companies put these data-driven strategies into action: how Pandora used second- and third-hand data to learn more about its listeners; how Georgia-Pacific moved from scarcity to abundance in the data sphere; and how Dunkin’ Brands leveraged CRM data as a force multiplier for customer engagement. And if you’re wondering what the future holds, you’ll receive seven forecasts to better prepare you for what may come next. Sure to be a classic, Data Driven is a practical road map to the modern marketing landscape and a toolkit for success in the face of changes already underway and still to come.

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Data Mining and Predictive Analytics
Daniel T. Larose

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

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Python Machine Learning
Sebastian Raschka

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

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R Graphics Cookbook
Winston Chang

This practical guide provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works. Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you’re ready to get started. Use R’s default graphics for quick exploration of data Create a variety of bar graphs, line graphs, and scatter plots Summarize data distributions with histograms, density curves, box plots, and other examples Provide annotations to help viewers interpret data Control the overall appearance of graphics Render data groups alongside each other for easy comparison Use colors in plots Create network graphs, heat maps, and 3D scatter plots Structure data for graphing

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Tidy Modeling with R
Max Kuhn, Julia Silge

Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work. RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people. With this book, you will: Learn the steps necessary to build a model from beginning to end Understand how to use different modeling and feature engineering approaches fluently Examine the options for avoiding common pitfalls of modeling, such as overfitting Learn practical methods to prepare your data for modeling Tune models for optimal performance Use good statistical practices to compare, evaluate, and choose among models

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Superintelligence - Paths, Dangers, Strategies
Nick Bostrom

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence.

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Practical DataOps - Delivering Agile Data Science at Scale
Harvinder Atwal

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

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Data Teams - A Unified Management Model for Successful Data-Focused Teams
Jesse Anderson

Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does. Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management. Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance. This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project. What You Will Learn Discover the three teams that you will need to be successful with big data Understand what a data scientist is and what a data science team does Understand what a data engineer is and what a data engineering team does Understand what an operations engineer is and what an operations team does Know how the teams and titles differ and why you need all three teams Recognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects Who This Book Is For Management, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects which may be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place. This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.

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Architecting Modern Data Platforms - A Guide to Enterprise Hadoop at Scale
Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George

There's a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you'll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You'll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

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A Mind For Numbers - How to Excel at Math and Science (Even If You Flunked Algebra)
Barbara Oakley, PhD

The companion book to COURSERA®'s wildly popular massive open online course "Learning How to Learn" Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a new skill set, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating material. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life. In A Mind for Numbers, Dr. Oakley lets us in on the secrets to learning effectively—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. The learning strategies in this book apply not only to math and science, but to any subject in which we struggle. We all have what it takes to excel in areas that don't seem to come naturally to us at first, and learning them does not have to be as painful as we might think.

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An Introduction to Statistical Learning - with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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Data Science for Business - What You Need to Know about Data Mining and Data-Analytic Thinking
Foster Provost, Tom Fawcett

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

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Hands-On Machine Learning with Scikit-Learn and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron

Graphics in this book are printed in black and white. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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Designing Machine Learning Systems
Chip Huyen

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems

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