Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).
The Make: Math Teacher's Supplement is a guide for teachers, parents and others who are exploring teaching with the authors' Make: Geometry, Make: Trigonometry, or Make: Calculus books. It covers the philosophy behind the books as well as practical tips for managing student 3D printed workflow, classroom technology needed, assessing student understanding, and similar topics. The authors include a list of learning objectives by chapter for all three books, and a matrix of topics covered to simplify adding these materials to existing lesson plans. This guide draws on the the authors' experience training teachers to use 3D printers and OpenSCAD (the math modeling software used in the other books) to summarize what a teacher needs to know before class starts, and tips on learning enough to stay ahead of the students as they explore the 3D printable and other models in the book series. Note that this supplement presumes that the reader has one or more of the author's Make: mathematics books. It is not a "Teacher's Edition" which repeats the content of the regular edition books. Those must be purchased separately.
Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications. Understand LLM architecture and learn how to best interact with it Design a complete prompt-crafting strategy for an application Gather, triage, and present context elements to make an efficient prompt Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG
Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries such as transformers and diffusers, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model or fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train a model that can write text based on your own unique style
Already popular among programmers for its memory safety and speed, the Rust programming language is also valuable for asynchrony. This practical book shows you how asynchronous Rust can help you solve problems that require multitasking. You'll learn how to apply async programming to solve problems with an async approach. You will also dive deeper into async runtimes, implementing your own ways in which async runtimes handle incoming tasks. Authors Maxwell Flitton and Caroline Morton also show you how to implement the Tokio software library to help you with incoming traffic, communicate between threads with shared memory and channels, and design a range of complex solutions using actors. You'll also learn to perform unit and end-to-end tests on a Rust async system. With this book, you'll learn: How Rust approaches async programming How coroutines relate to async Rust Reactive programming and how to implement pub sub in async rust How to solve problems using actors How to customize Tokio to gain control over how tasks are processed Async Rust design patterns How to build an async TCP server just using the standard library How to unit test async Rust By the end of the book, you'll be able to implement your own async TCP server completely from the standard library with zero external dependencies, and unit test your async code.
Learn the 5% of Python programming knowledge which exponentially accelerates your learning curve for the remaining 95%. Alleviate the overwhelm of "too much to learn, not enough time". This book cuts through the noise to focus on the "accelerators" that rapidly move the needle. Discover Python's key abstractions which enable and power the most important Python libraries, including Pandas; Django; Flask; SQLAlchemy; Twisted; Pytest; and more. Top Python programming performance is closer than you think. The difference between the best and the rest lies in the distinctions they make, the mental models they leverage, and their ability to perceive what others cannot. Powerful Python cuts through the noise to focus on these performance accelerators which rapidly improve your coding level and yield the most benefit on real-world production engineering and data teams. After a brief tour of the most necessary programming fundamentals; coding techniques and libraries including Pandas, Django, Flask, SQLAlchemy, Twisted, and Pytest are explored to unlock huge capabilities for the reader. Complex patterns are explained with only the minimum detail needed for use, and frequent code examples are used to show all methods in practice. For those ready to move beyond junior programmer stage, this book provides the 5% of knowledge that makes the remaining 95% of the journey a walk in the park.
This book is a guide to DevOps and software delivery: that is, a guide to the numerous tools and techniques that are required to take that application code and run it and maintain it in production, where it can generate value for your users and your company on an ongoing basis. This includes going through all the modern practices for deploying applications and microservices to the cloud, managing your infrastructure as code, automating your software delivery lifecycle in a CI/CD pipeline, configuring networking, setting up data stores, and hooking up monitoring.
DuckDB is an open source in-process database created for OLAP workloads. It provides key advantages that separate this database from more mainstream OLAP solutions, including embeddability, compatibility with SQL, optimization for fast and efficient analytics, and integration with Python. This practical book shows you how DuckDB leverages Python libraries and tools for data analytics, machine learning, and AI. Author Wei-Meng Lee shows developers, data engineers, data analysts, and data scientists how to get started. You'll learn the primary features and functions of DuckDB, explore use cases and best practices, and examine practical examples of how DuckDB can be used for a variety of data analytics tasks. You'll also dive into specific topics including how to import data into DuckDB, work with tables, perform exploratory data analysis, visualize DuckDB data, perform spatial analysis, and use DuckDB with JSON files, Polars, and JupySQL. You'll also explore: The purpose of DuckDB and its main functions How to conduct data analytics tasks using DuckDB Methods for integrating DuckDB with pandas, Polars, and JupySQL How to use DuckDB to query your data Ways to perform spatial analytics using DuckDB's spatial extension How to work with a diverse range of data including Parquet, CSV, and JSON Wei-Meng Lee is a technologist and founder of Developer Learning Solutions, a company that provides hands-on training on the latest technologies.
Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient.Author Brendan BurnsDirector of Engineering at Microsoft Azuredemonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system.Understand how patterns and reusable components enable the rapid development of reliable distributed systemsUse the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machineExplore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the componentsLearn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows
Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks. You'll learn: Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products How to develop an intuition about the Transformer architecture and the impact of each architectural decision Ways to adapt pretrained language models to your own domain and use cases How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques How to interface language models with external tools and integrate them into an existing software ecosystem
Take your web development skills from browser to server with Node.js, the popular backend framework used by more than 10 million developers at companies like Amazon, Netflix, and LinkedIn, to name just a few. If you're comfortable working with JavaScript, this practical guide from Samer Buna will show you how to effectively build and maintain even the most complex Node.js applications. Following a hands-on, project-based approach, you'll move from key fundamentals to advanced concepts such as modules, packages, event-driven architecture, streams, child processes, scaling, testing, deployment, and much more--all while focusing on what actually matters in practice. Explore Node.js modules and packages Understand Node.js's event-driven architecture, streams, and child processes Create, test, and maintain efficient and scalable Node.js applications
Business decisions in any context--operational, tactical, or strategic--can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty. With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action--what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future. While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you: The distinction among descriptive, predictive, and prescriptive analytics How predictive analytics produces a menu of action options How prescriptive analytics narrows the menu of action options The forms of prescriptive analytics: eight prescriptive methods Two broad classes of these methods: non-stochastic and stochastic How to develop prescriptive analyses for action recommendations Ways to use an appropriate tool-set in Python
Make: The Complete Guide to Tinkercad embraces the concept of "learn by doing," using 17 fun projects to transform the reader from a novice into a creator of video assets and objects ready for 3D printing. This comprehensive manual teaches the intricacies of the Tinkercad interface, how to model sophisticated objects, and use AI as a tool to solve design challenges. Projects include designing useful and amusing objects such as jewelry, toys, and practical household items. This is not a mere collection of generic online tutorials, but a comprehensive learning experience that will empower the reader to turn their ideas into a physical reality.
What will you learn from this book?This brain-friendly guide teaches you everything from JavaScript language fundamentals to advanced topics, including objects, functions, and the browsers document object model. You wont just be readingyoull be playing games, solving puzzles, pondering mysteries, and interacting with JavaScript in ways you never imagined. And youll write real code, lots of it, so you can start building your own web applications. Prepare to open your mind as you learn (and nail) key topics including:The inner details of JavaScriptHow JavaScript works with the browserThe secrets of JavaScript typesUsing arraysThe power of functionsHow to work with objectsMaking use of prototypesUnderstanding closuresWriting and testing applicationsWhats so special about this book?We think your time is too valuable to waste struggling with new concepts. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First JavaScript Programming uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep. This book replaces Head First JavaScript, which is now out of print.
Deriving business value from analytics is a challenging process. Turning data into information requires a business analyst who is adept at multiple technologies including databases, programming tools, and commercial analytics tools. This practical guide shows programmers who understand analysis concepts how to build the skills necessary to achieve business value. Author Deanne Larson, data science practitioner and academic, helps you bridge the technical and business worlds to meet these requirements. You'll focus on developing these skills with R and Python using real-world examples. You'll also learn how to leverage methodologies for successful delivery. Learning methodology combined with open source tools is key to delivering successful business analytics and value. This book shows you how to: Apply business analytics methodologies to achieve successful results Cleanse and transform data using R and Python Use R and Python to complete exploratory data analysis Create predictive models to solve business problems in R and Python Use Python, R, and business analytics tools to handle large volumes of data Commit code to GitHub to collaborate with data engineers and data scientists Measure success in business analytics
Learn how to implement and manage a modern customer data platform (CDP) through the Salesforce Data Cloud. This practical book provides a comprehensive overview that shows architects, administrators, developers, data engineers, and marketers how to ingest, store, and manage real-time customer data with the Data Cloud. Author Joyce Kay Avila demonstrates how to use Salesforce's native connectors, canonical data model, and built-in trust layer to accelerate your time to value. You'll learn how to leverage Salesforce's no-code / low-code functionality to expertly build a Data Cloud foundation that allows you to utilize AI to its fullest, not only within the Salesforce platform but also with third-party machine learning services like AWS SageMaker and Google Vertex. This book will help you: Develop a plan to execute a CDP project effectively and efficiently Connect Data Cloud to external data sources and build out a Customer 360 Data Model Leverage data sharing capabilities with Snowflake, BigQuery, Databricks, and Azure Use Salesforce Data Cloud capabilities for identity resolution and segmentation Create calculated, streaming, visualization, and predictive insights Use Data Graphs to power Salesforce Einstein capabilities Learn Data Cloud best practices for all phases of the development lifecycle
Cloud services and SaaS software permeate every company's IT landscape, requiring a shift from manually provisioned services to a more structured approach, with codification at its core. Terraform provides tools to manage the lifecycle of your IT landscape across thousands of different cloud providers and SaaS platforms. By defining your infrastructure as code you can safely and predictably make changes, modularize crucial building blocks, and create reusable service components. Each recipe in this cookbook addresses a specific problem and prefaces the solution with detailed insights into the "how" and "why". If you're just starting with Terraform and codified infrastructure, this book will help you create a solid foundation, on which you can build for years to come. If you're an advanced user, this guide will help you reaffirm your knowledge and take it to the next level, as you challenge yourself with more complex infrastructure, spread across multiple providers. Recipes include: Strategies on how to use Terraform with Version Control Systems Validation and testing patterns for Terraform-managed infrastructure Methods for importing pre-existing resources Transforming infrastructure services into reusable components Integrating Terraform with other HashiCorp tools Deploying Containerized Workloads
This concise yet comprehensive guide explains how to adopt a data lakehouse architecture to implement modern data platforms. It reviews the design considerations, challenges, and best practices for implementing a lakehouse and provides key insights into the ways that using a lakehouse can impact your data platform, from managing structured and unstructured data and supporting BI and AI/ML use cases to enabling more rigorous data governance and security measures. Practical Lakehouse Architecture shows you how to: Understand key lakehouse concepts and features like transaction support, time travel, and schema evolution Understand the differences between traditional and lakehouse data architectures Differentiate between various file formats and table formats Design lakehouse architecture layers for storage, compute, metadata management, and data consumption Implement data governance and data security within the platform Evaluate technologies and decide on the best technology stack to implement the lakehouse for your use case Make critical design decisions and address practical challenges to build a future-ready data platform Start your lakehouse implementation journey and migrate data from existing systems to the lakehouse
> > > The basic rules for classic charts that are used in 90% of business reports Exceptions to general rules based on real business cases Best practices for dashboard design How to properly set up interactions How to prepare data for advanced visuals How to avoid pitfalls with eye-catching charts
"Performance tuning is an experimental science, but that doesn't mean engineers should resort to guesswork and folklore to get the job done. Yet that's often the case. With this practical book, intermediate to advanced Java technologists working with complex platforms will learn how to tune Java cloud applications for performance using a quantitative, verifiable, and repeatable approach. In response to the ubiquity of cloud computing, this updated edition of Optimizing Cloud Native Java addresses topics that are key to high performance of Java applications in the cloud. Many resources on performance tend to focus on the theory and internals of Java virtual machines, but this book discusses the low-level technical aspects within the context of performance-tuning practicalities and examines a wide range of aspects. With this book, you will: Learn how Java principles and technology make the best use of modern hardware, operating systems, and cloud stacks; Examine the pitfalls of measuring Java performance numbers and the drawbacks of microbenchmarking; Understand how to package, deploy, operate, and debug Java/JVM applications in modern cloud environments; Apply emerging observability approaches to obtain deep understanding of cloud native applications"--
> Inside Cyber Warfare features an exclusive deep dive into the wartime operations of an offensive cyber unit of Ukraine's Ministry of Defense as it works to defend the nation against Russian forces, particularly since the 2022 invasion: See what happened when a Ukrainian cyber and special operations team worked together to destroy a secret missile laboratory Explore the legal status of cyber warfare and civilian hackers Discover how a cyber team with little money and limited resources learned to create fire from the manipulation of code in automated systems Distinguish reality from fiction regarding AI safety and existential risk Learn new strategies for keeping you and your loved ones safe in an increasingly complex and insecure world
Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical view of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer who specializes in finance not only has specific data engineering knowledge, but also a good understanding of financial domain-specific problems, approaches, data ecosystem, data providers, data formats, technological constraints, identifiers, entities, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering with real use cases, market practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering Structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source and cloud technologies About the author: Tamer Khraisha is a senior software and data engineer and scientific author with over a decade of experience in the financial sector and academia.
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets. Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition. With this definitive guide at your side, you'll be able to: Process larger-than-memory datasets at record speed Apply the eager, lazy, and streaming APIs of Polars and decide when to use them Transition smoothly from pandas or Spark to Polars Integrate Polars into your existing code base Work with Arrow and Parquet to efficiently read and write data Translate complex ETL tasks into efficient and elegant queries
With the increasing complexity of modern cloud-based systems, an effective enterprise architecture program is more critical than ever. In this practical book, author Tanu McCabe from Capital One provides proven frameworks and practices to define an effective enterprise architecture strategy--one that will enable software and enterprise architects to create and implement great architecture strategies. You'll learn how to create shared alignment across business and technology, embed architecture practices into processes and tooling, be proactive and reactive to technology and business trends, and instill contextual understanding over siloed decision-making. Complete with examples of patterns and antipatterns, this book provides reusable templates, assessment tools, and practical advice. With this book, you will: Understand exactly what enterprise architecture is, and why it's important to build an effective enterprise architecture practice Learn who needs to be involved to define and implement architecture strategies Examine common pitfalls that inhibit effective architecture strategies Assess the current state of your organization's architecture practice to identify opportunities for improvement Define your own architecture strategy at both an organizational and personal level by applying the book's frameworks Enhance your ability to make great architecture decisions using the book's frameworks and lessons learned Tanusree (Tanu) McCabe is an Executive Distinguished Engineer who leads enterprise architecture strategy at Capital One.
Discover how Delta Lake simplifies the process of building data lakehouses and data pipelines at scale. With this practical guide, data engineers, data scientists, and data analysts will explore key data reliability challenges and learn to apply modern data engineering and management techniques. You'll also understand how ACID transactions bring reliability to data lakehouses at scale. Authors Denny Lee, Prashanth Babu, Tristen Wentling, and Scott Haines explain how to harness the power of Delta Lake to increase your data productivity at scale. You'll learn how to run batch and streaming jobs concurrently on your data lake and accelerate the usability of your data by building effective and high-quality end-to-end pipelines, from data ingestion to analytics. This book helps you: Understand key data reliability challenges Examine data management and engineering techniques using the modern data stack Realize data reliability improvements using Delta Lake Concurrently run streaming and batch jobs against your data lake Execute update, delete, and merge commands Use time travel to rollback and examine previous versions of your data Build a streaming data quality pipeline following the medallion construct About the authors: Denny Lee is a Delta Lake maintainer and Apache Spark and MLflow contributor. Prashanth Babu is a Delta practitioner who works at Databricks. Tristen Wentling is a Delta practitioner who works at Databricks. Scott Haines is an Apache Spark and Delta Lake contributor who works at Nike.
If you're a developer looking to build a distributed, resilient, scalable, high-performance application, you may be evaluating distributed SQL and NoSQL solutions. Perhaps you're considering the Aerospike database. This practical book shows developers, architects, and engineers how to get the highly scalable and extremely low-latency Aerospike database up and running. You will learn how to power your globally distributed applications and take advantage of Aerospike's hybrid memory architecture with the real-time performance of in-memory plus dependable persistence. After reading this book, you'll be able to build applications that can process up to tens of millions of transactions per second for millions of concurrent users on any scale of data. This practical guide provides: Step-by-step instructions on installing and connecting to Aerospike A clear explanation of the programming models available All the advice you need to develop your Aerospike application Coverage of issues such as administration, connectors, consistency, and security Code examples and tutorials to get you up and running quickly And more
The software architect role is evolving. As systems and distributed teams become more complex, it's often impossible for architects to be everywhere they need to be. To be effective, consultants and in-house architects alike have to move constantly from client to client or team to team to collaborate and work with code. And the situation is reaching a breaking point. There's a better way. Andrew Harmel-Law, tech principal at Thoughtworks, shows you how architects and development teams can collaborate effectively and efficiently on the architectures of their systems. Techniques in this book help you ensure that everyone and everything is working toward the same goal. You'll learn how to create a collaborative, decentralized mindset that allows everyone to do architecture and build the best systems they've ever experienced. With this book, you will: Understand the new dynamics that affect modern software delivery and how to take advantage of them to optimize for fast flow and continuous feedback Learn a methodology that brings software architecture and development together in partnership Nurture the fundamental interplay of decisions, advice, autonomy, and architecture Initiate practices and constraints that maximize benefits and mitigate risks Create an approach tuned to your skill sets, architecture, and your organization's engineering culture Identify and work to prevent failure modes when they threaten to arise
To ensure that applications are reliable and always available, more businesses today are moving applications to AWS. But many companies still struggle to design and build these cloud applications effectively, thinking that because the cloud is resilient, their applications will be too. With this practical guide, software, DevOps, and cloud engineers will learn how to implement resilient designs and configurations in the cloud using hands-on independent labs. Authors Kevin Schwarz, Jennifer Moran, and Dr. Nate Bachmeier from AWS teach you how to build cloud applications that demonstrate resilience with patterns like back off and retry, multi-Region failover, data protection, and circuit breaker with common configuration, tooling, and deployment scenarios. Labs are organized into categories based on complexity and topic, making it easy for you to focus on the most relevant parts of your business. You'll learn how to: Configure and deploy AWS services using resilience patterns Implement stateless microservices for high availability Consider multi-Region designs to meet business requirements Implement backup and restore, pilot light, warm standby, and active-active strategies Build applications that withstand AWS Region and Availability Zone impairments Use chaos engineering experiments for fault injection to test for resilience Assess the trade-offs when building resilient systems, including cost, complexity, and operational burden
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