Use the Infrastructure as Code (IaC) approach to automate, test, and streamline infrastructure for mission-critical applications.In Essential Infrastructure as Code youwill learn how to:Optimize infrastructure for modularity and isolate dependenciesTest infrastructure configurationMitigate, troubleshoot, and isolate failed infrastructure changesCollaborate across teams on infrastructure developmentUpdate infrastructure with minimal downtime using blue-green deploymentsScale infrastructure systems supporting multiple business unitsUse patterns for provisioning tools, configuration management, and image buildingDeliver secure infrastructure configuration to productionEssential Infrastructure as Code teaches you how to automate software infrastructure by capturing your desired configurations as a set of scripts.Youll learn how to create, test, and deploy infrastructure components in a way thats easy to scale and share across an entire organization. While the patterns and techniques are tool agnostic, youll appreciate the easy-to-follow examples in Python and Terraform. A system administrator or infrastructure engineer will learn essential software development practices for managing IaC, while developers will benefit from in-depth coverage of assembling and monitoring infrastructure as part of DevOps culture.
AI models can become so complex that even experts have difficulty understanding themand forget the nuances of a cluster of novel algorithms to a business stakeholder! Fortunately, there are techniques and best practices that will help make your AI systems transparent and interpretable. Interpretable AI is filled with cutting-edge techniques that will improve your understanding of how your AI models function. Focused on practical methods that you can implement with Python, it teaches you to open up the black box of machine learning so that you can combat data leakage and bias, improve trust in your results, and ensure compliance with legal requirements. Youll learn to identify when to utilize models that are inherently transparent, and how to mitigate opacity when youre facing a problem that demands the predictive power of ahard-to-interpret deep learning model.
When youGÇÖre new to Python, it can be tough to understand where and how to use its many language features. ThereGÇÖs a dizzying array of libraries, and itGÇÖs challenging to fit everything together. The Well-Grounded Python Developer builds on Python skills youGÇÖve learned in isolation and shows you how to unify them into a meaningful whole. As you work through this practical guide, youGÇÖll discover how all the bits of the Python ecosystem connect as you build and modify a typical web server application.
Kubernetes has changed everything about deploying applications to the cloudfor the better! Kubernetes for Developers is a clear and practical beginners guide that shows you just how easy, flexible, and cost-effective it can be to make the switch to Kubernetes deployment even for small to medium-sized applications. Youll learn how to migrate your existing apps onto Kubernetes without a rebuild, and implement modern cloud native architectures that can handle your future growth. Youll take advantage of the powerful automation tools in Google Kubernetes Engine to perform automatic checks and scaling, giving you more time to spend developing great applications!
Domain-specific languages are custom text orgraphical interfaces that allow domain experts to create and modify their own software systems. With a syntax that's clear and familiar to the non-technical user, DSLs are precise enough to generate working software in traditional codewith. Written for developers who need to create user-facing DSLs, Domain-SpecificLanguages Made Easy unlocks clear and practical methods to create DSLswith easy-to-use interfaces. By working through a detailed example of a car rental ompany, you'll see how creating a custom DSL can get rid of time-consuming and bureaucratic code adjustments, freeing you up to work on features whilst your clients and colleagues write their software themselves!
Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner.In Automated Machine Learning in Action you will learn how to:Improve a machine learning model by automatically tuning its hyperparametersPick the optimal components for creating and improving your pipelinesUse AutoML toolkits such as AutoKeras and Keras TunerDesign and implement search algorithms to find the best component for your ML taskAccelerate the AutoML process with data-parallel, model pre-training, and other techniquesAutomated Machine Learning in Action reveals how premade machine learning components can automate time-consuming ML tasks.Its written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. Youll use tools like AutoKeras to create pipelines that automatically select the best approach for your task, remove the burden of manual tuning, and can even be implemented by machine learning novices!
When it comes to data analytics, itpays to think big. PySpark blends the powerful Spark big data processing engine with the Python programming language to provide a data analysis platform that can scale up for nearly any task. Data Analysis with Python and PySparkis your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build lightning-fast pipelines for reporting, machine learning, and otherdata-centric tasks. No previous knowledge of Spark is required.
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.
Many enterprise applications intertwine code that defines an apps behaviour with code that defines its network communication and other non-functional concerns. The service mesh pattern, implemented by platforms like Istio, helps you push operational issues into the infrastructure so the application code is easier to understand, maintain, and adapt.Istio in Action teaches you how to implement a full-featured Istio-based service mesh to manage a microservices application. With the skills you learn in this comprehensive tutorial, youll be able to delegate the complex infrastructure of your cloud-native applications to Istio!
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!
A practical field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away.
Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt.In Good Code, Bad Code youll learn how to:Think about code like an effective software engineerWrite functions that read like well-structured sentencesEnsure code is reliable and bug freeEffectively unit test codeIdentify code that can cause problems and improve itWrite code that is reusable and adaptable to new requirementsImprove your medium and long-term productivitySave yourself and your team time The difference between good code or bad code often comes down tohow you apply the established practices of the software development community.In Good Code, Bad Code youll learn how to boost your productivity and effectiveness with code development insights normally only learned through careful mentorship and hundreds of code reviews.
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!
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.
In Software Telemetry you will learn how to:Manage toxic telemetry and confidential recordsMaster multi-tenant techniques and transformation processesUpdate to improve the statistical validity of your metrics and dashboardsMake software telemetry emissions easier to parseBuild easily-auditable logging systemsPrevent and handle accidental data leaksMaintain processes for legal complianceJustify increased spend on telemetry softwareSoftware Telemetry teaches you best practices for operating andupdating telemetry systems. These vital systems trace, log, and monitor infrastructure by observing and analysing the events generated by the system.This practical guide is filled with techniques you can apply to any size of organization, with troubleshooting techniques for every eventuality, and methods to ensureyour compliance with standards like GDPR.
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
Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services.In Conversational AI, you will learn how to:Pick the right AI assistant type and channel for your needsWrite dialog with intentional tone and specificityTrain your AIs classifier from the ground upCreate question-and-direct-response AI assistantsDesign and optimize a process flow for web and voiceTest your assistants accuracy and plan out improvementsConversational AI: Chatbots that work teaches you to create the kind of AI-enabled assistants that are revolutionizing the customer service industry. Youll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. This engaging and entertaining book delivers the essential technical and creative skills for designing successful AI solutions, from coding process flows and training machine learning, to improving your written dialog.
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.
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