Books published by Manning Publications

Software development isn't an "ivory tower" exercise. Street coders get the job done by prioritizing tasks, making quick decisions, and knowing which rules to break.Street Coder: Rules to break and how to break them is a programmer's survival guide, full of tips,tricks, and hacks that will make you a more efficient programmer. This book's rebel mindset challenges status quo thinking and exposes the important skills you need on the job. You'll learn the crucial importance of algorithms and data structures, turn programming chores into programming pleasures, and shatter dogmatic principles keeping you from your full potential.

Customer-facing and internal APIs have become the most common wayto integrate the components of web-based software. Using standards like OpenAPI, you can provide reliable, easy-to-use interfaces that allow other developers safe, controlled access to your software. Designing APIs with Swagger and OpenAPI is a hands-on primer to properly designing and describing your APIs using the most widely-adopted standard.

Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits.In Privacy Engineering youwill learn how to:Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and boardPrivacy Engineering teaches you to implement technical privacy solutions and tools at scale. Youll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen the privacy programs at Google, Netflix, and Uber. Youll find technical methods that can be instantly applied to almost any system, and improve your user privacy without spiraling time and resource costs.

Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction.In Machine Learning Engineering inAction, you will learn: Evaluatingdata science problems to find the most effective solution Scopinga machine learning project for usage expectations and budget Processtechniques that minimize wasted effort and speed up production Assessinga project using standardized prototyping work and statistical validation Choosingthe right technologies and tools for your project Makingyour codebase more understandable, maintainable, and testable Automatingyour troubleshooting and logging practices Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. YouGÇÖll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!

Your brain responds in a predictable way when it encounters new or difficult tasks. This unique book teaches you concrete techniques rooted incognitive science that will improve the way you learn and think about code.In The Programmers Brain:What every programmer needs to know about cognition you will learn:Fast and effective ways to master new programming languagesSpeed reading skills to quickly comprehend new code Techniques to unravel the meaning of complex codeWays to learn new syntax and keep it memorizedWriting code that is easy for others to readPicking the right names for your variablesMaking your codebase more understandable to newcomersOnboarding new developers to your teamLearn how to optimize your brains natural cognitive processes to read code more easily, write code faster, and pick up new languages in much less time. This book will help you through the confusion you feel when faced with strange and complex code, and explain a code base inways that can make a new team member productive in days!

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library.Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading.

Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.In Inside Deep Learning, you will learn how to:Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problemsInside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skippedyoull dive into math, theory, and practical applications. Everything is clearly explained in plain English.

From its humble beginnings a container orchestration system, Kubernetes has become the de facto infrastructure for cloud native applications. Kubernetes impacts every aspect of the application development lifecycle, from design through deployment. To build and operate reliable cloud native systems, you need to understand whats going on below the surface. Core Kubernetes is packed with experience-driven insights and practical techniques, and takes you inside Kubernetes to teach you what youll need to know to keep your system running like a well-oiled machine and prevent those panicked 3 AM phone calls.

Learn how to think about your development pipeline as amission-critical application, with techniques for implementing code-driven infrastructure and CI/CD systems using Jenkins, Docker, Terraform, andcloud-native services. In Pipeline as Code, you will master: Building and deploying a Jenkins cluster from scratch Writing pipeline as code for cloud native applications Automating the deployment of Dockerized and Serverless applications Containerizing applications with Docker and Kubernetes Deploying Jenkins on AWS, GCP and Azure Managing, securing and monitoring a Jenkins cluster in production Key principles for a successful DevOps culture Pipeline as Code is apractical guide to automating your development pipeline in a cloud-native, service-driven world. YouGÇÖll use the latest infrastructure-as-code tools likePacker and Terraform to develop reliable CI/CD pipelines for numerous cloud-native applications. Follow this book's insightful best practices, and youGÇÖll soon be delivering software thatGÇÖs quicker to market, faster to deploy,and with less last-minute production bugs.

Learn PowerShell in a Month of Lunches covers Windows, Linux, and macOS is a task-focused tutorial for administering Linuxand macOS systems using Microsoft PowerShell. Adapted by PowerShell team members Travis Plunk and Tyler Leonhardt from the best selling Learn Windows PowerShell in a Month of Lunches by community legends DonJones and Jeffrey Hicks, it features Linux-based examples covering core language features and admin tasks. Designed for busy IT professionals, this innovative guide will take you from the basics to PowerShell proficiency through 25 tutorials you can do in your lunch break

Build fast, efficient Kubernetes-based Java applications using the Quarkus framework, MicroProfile, and Java standards.Most popular Java frameworks, like Spring, were designed long before the advent of Kubernetes and cloud-native systems. A new generation of tools, including Quarkus and MicroProfile have been cloud-native and Kubernetes-aware from the beginning. Kubernetes Native Microservices: With Quarkus and MicroProfile teaches you how to create efficient enterprise Java applications that are easy to deploy, maintain, and expand.In Kubernetes Native Microservices: With Quarkus and MicroProfile youll learn how to:Deploy enterprise Java applications on KubernetesDevelop applications using the Quarkus runtime frameworkCompile natively using GraalVM for blazing speedCreate efficient microservices applicationsTake advantage of MicroProfile specifications

Logging in Action teaches you how to make your log processing a real asset for your application, all with free and open source tools. YouGÇÖll use the powerful log management tool Fluentd to solve common log problems, and learn how proper log management can improve performance and make management of software solutions easier. Through useful examples like sending log driven events to Slack, youGÇÖll get hands-on experience applying structure to your unstructured data.

Grokking Functional Programming is a practical book written especially for object-oriented programmers. It will help you map familiar ideas like objects and composition to FP concepts such as programming with immutable data and higher-order functions. You will learn how to write concurrent programs, how to handle errors and how to design your solutions with modularity and readability in mind. And you'll be pleased to know that we skip the academic baggage of lambda calculus, category theory, and the mathematical foundations of FP in favour of applying functional programming to everyday programming tasks. At the end of the book, you'll be ready to pick a functional language and start writing useful and maintainable software.

Five Lines of Code teaches refactoring that's focused on concrete rules and getting any method down to five lines or less! There’s no jargon or tricky automated-testing skills required, just easy guidelines and patterns illustrated by detailed code samples.In Five Lines of Code you will learn:     The signs of bad code     Improving code safely, even when you don’t understand it     Balancing optimization and code generality     Proper compiler practices     The Extract method, Introducing Strategy pattern, and many other refactoring patterns     Writing stable code that enables change-by-addition     Writing code that needs no comments     Real-world practices for great refactoring Improving existing code—refactoring—is one of the most common tasks you’ll face as a programmer. Five Lines of Code teaches you clear and actionable refactoring rules that you can apply without relying on intuitive judgements such as “code smells.” Following the author’s expert perspective—that refactoring and code smells can be learned by following a concrete set of principles—you’ll learn when to refactor your code, what patterns to apply to what problem, and the code characteristics that indicate it’s time for a rework. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Every codebase includes mistakes and inefficiencies that you need to find and fix. Refactor the right way, and your code becomes elegant, easy to read, and easy to maintain. In this book, you’ll learn a unique approach to refactoring that implements any method in five lines or fewer. You’ll also discover a secret most senior devs know: sometimes it’s quicker to hammer out code and fix it later! About the book Five Lines of Code is a fresh look at refactoring for developers of all skill levels. In it, you’ll master author Christian Clausen’s innovative approach, learning concrete rules to get any method down to five lines—or less! You’ll learn when to refactor, specific refactoring patterns that apply to most common problems, and characteristics of code that should be deleted altogether. What's inside     The signs of bad code     Improving code safely, even when you don’t understand it     Balancing optimization and code generality     Proper compiler practices About the reader For developers of all skill levels. Examples use easy-to-read Typescript, in the same style as Java and C#. About the author Christian Clausen works as a Technical Agile Coach, teaching teams how to refactor code. Table of Contents 1 Refactoring refactoring 2 Looking under the hood of refactoring PART 1 LEARN BY REFACTORING A COMPUTER GAME 3 Shatter long function 4 Make type codes work 5 Fuse similar code together 6 Defend the data PART 2 TAKING WHAT YOU HAVE LEARNED INTO THE REAL WORLD 7 Collaborate with the compiler 8 Stay away from comments 9 Love deleting code 10 Never be afraid to add code 11 Follow the structure in the code 12 Avoid optimizations and generality 13 Make bad code look bad 14 Wrapping up

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems.Summary In Transfer Learning for Natural Language Processing you will learn:     Fine tuning pretrained models with new domain data     Picking the right model to reduce resource usage     Transfer learning for neural network architectures     Generating text with generative pretrained transformers     Cross-lingual transfer learning with BERT     Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside     Fine tuning pretrained models with new domain data     Picking the right model to reduce resource use     Transfer learning for neural network architectures     Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

Knative in Action teaches you to build complex and efficient serverless applications.Summary Take the pain out of managing serverless applications. Knative, a collection of Kubernetes extensions curated by Google, simplifies building and running serverless systems. Knative in Action guides you through the Knative toolkit, showing you how to launch, modify, and monitor event-based apps built using cloud-hosted functions like AWS Lambda. You’ll learn how to use Knative Serving to develop software that is easily deployed and autoscaled, how to use Knative Eventing to wire together disparate systems into a consistent whole, and how to integrate Knative into your shipping pipeline. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology With Knative, managing a serverless application’s full lifecycle is a snap. Knative builds on Kubernetes orchestration features, making it easy to deploy and run serverless apps. It handles low-level chores—such as starting and stopping instances—so you can concentrate on features and behavior. About the book Knative in Action teaches you to build complex and efficient serverless applications. You’ll dive into Knative’s unique design principles and grasp cloud native concepts like handling latency-sensitive workloads. You’ll deliver updates with Knative Serving and interlink apps, services, and systems with Knative Eventing. To keep you moving forward, every example includes deployment advice and tips for debugging. What's inside     Deploy a service with Knative Serving     Connect systems with Knative Eventing     Autoscale responses for different traffic surges     Develop, ship, and operate software About the reader For software developers comfortable with CLI tools and an OO language like Java or Go. About the author Jacques Chester has worked in Pivotal and VMWare R&D since 2014, contributing to Knative and other projects. Table of Contents 1 Introduction 2 Introducing Knative Serving 3 Configurations and Revisions 4 Routes 5 Autoscaling 6 Introduction to Eventing 7 Sources and Sinks 8 Filtering and Flowing 9 From Conception to Production

If you're browsing the web, using public APIs, making and receiving electronic payments, registering and logging in users, or experimenting with blockchain, you're relying on cryptography. And you're probably trusting a collection of tools, frameworks, and protocols to keep your data, users, and business safe. It's important to understand these tools so you can make the best decisions about how, where, and why to use them. Real-World Cryptography teaches you applied cryptographic techniques to understand and apply security at every level of your systems and applications.about the technologyCryptography is the foundation of information security. This simultaneously ancient and emerging science is based on encryption and secure communication using algorithms that are hard to crack even for high-powered computer systems. Cryptography protects privacy, secures online activity, and defends confidential information, such as credit cards, from attackers and thieves. Without cryptographic techniques allowing for easy encrypting and decrypting of data, almost all IT infrastructure would be vulnerable.about the bookReal-World Cryptography helps you understand the cryptographic techniques at work in common tools, frameworks, and protocols so you can make excellent security choices for your systems and applications. There's no unnecessary theory or jargon-just the most up-to-date techniques you'll need in your day-to-day work as a developer or systems administrator. Cryptography expert David Wong takes you hands-on with cryptography building blocks such as hash functions and key exchanges, then shows you how to use them as part of your security protocols and applications. Alongside modern methods, the book also anticipates the future of cryptography, diving into emerging and cutting-edge advances such as cryptocurrencies, password-authenticated key exchange, and post-quantum cryptography. Throughout, all techniques are fully illustrated with diagrams and real-world use cases so you can easily see how to put them into practice. what's insideBest practices for using cryptographyDiagrams and explanations of cryptographic algorithmsIdentifying and fixing cryptography bad practices in applicationsPicking the right cryptographic tool to solve problemsabout the readerFor cryptography beginners with no previous experience in the field.about the authorDavid Wong is a senior engineer working on Blockchain at Facebook. He is an active contributor to internet standards like Transport Layer Security and to the applied cryptography research community. David is a recognized authority in the field of applied cryptography; he's spoken at large security conferences like Black Hat and DEF CON and has delivered cryptography training sessions in the industry.

Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you'll take on the challenge of learning functional programming from first principles, and start writing Kotlin code that's easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors.about the technologyKotlin is a new JVM language designed to interoperate with Java and offer an improved developer experience for creating new applications. It's already a top choice for writing web services, and Android apps. Although it preserves Java's OO roots, Kotlin really shines when you adopt a functional programming mindset. By learning the core principles and practices of functional programming outlined in this book, you'll start writing code that's easier to read, easier to test and reuse, better for concurrency, and less prone to bugs.about the bookFunctional Programming in Kotlin is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. Based on the bestselling Functional Programming in Scala, this book guides intermediate Java and Kotlin programmers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you'll find concrete examples and exercises that open up the world of functional programming. The book will deliver practical mastery of FP using Kotlin and a valuable perspective on program design that you can apply to other languages. what's insideFunctional programming techniques for real-world applicationsWrite combinator librariesIdentify common structures and idioms in functional designCode for simplicity, modularity, and fewer bugsabout the readerFor intermediate Kotlin and Java developers. No experience with functional programming is required.about the authorMarco Vermeulen has almost two decades of programming experience on the JVM, with much of that time spent on functional programming using Scala and Kotlin.Rúnar Bjarnason and Paul Chiusano are the authors of Functional Programming in Scala, on which this book is based. They are internationally-recognized experts in functional programming and the Scala programming language.

Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system's infrastructure. Following a real-world use case for calculating taxi fares, you'll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware.about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you're free to focus on tuning and improving your models.about the bookCloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You'll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you'll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you'll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you're done, you'll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's insideExtracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying trained models and pipelines as a service endpointMonitoring and managing your pipeline's life cycleMeasuring performance improvementsabout the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required.about the authorCarl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world's foremost experts in machine learning and also helped manage the company's efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl.

The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether.Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside     Calculating churn metrics     Identifying user behavior that predicts churn     Using churn reduction tactics with customer segmentation     Applying churn analysis techniques to other business areas     Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn

Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines.Summary A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You'll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline's needs. What's inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Scheduling in Airflow 4 Templating tasks using the Airflow context 5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows 7 Communicating with external systems 8 Building custom components 9 Testing 10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices 12 Operating Airflow in production 13 Securing Airflow 14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds 16 Airflow on AWS 17 Airflow on Azure 18 Airflow in GCP