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By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. Youll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works.In the process, youll learn the basics of Django, Selenium, Git, jQuery, and Mock, along with current web development techniques. If youre ready to take your Python skills to the next level, this bookupdated for Python 3.6clearly demonstrates how TDD encourages simple designs and inspires confidence.Dive into the TDD workflow, including the unit test/code cycle and refactoringUse unit tests for classes and functions, and functional tests for user interactions within the browserLearn when and how to use mock objects, and the pros and cons of isolated vs. integrated testsTest and automate your deployments with a staging serverApply tests to the third-party plugins you integrate into your siteRun tests automatically by using a Continuous Integration environmentUse TDD to build a REST API with a front-end Ajax interface

We've arrived in a new era--GenAI is reshaping industries and decision-making processes across the board. As a result, understanding its potential and pitfalls has become crucial. But in order to stay ahead of the curve, you'll need to develop fresh perspectives on leveraging AI beyond mere technical know-how. Geared toward business leaders and tech professionals alike, this book demystifies the strategic integration of AI into business practices, ensuring you're equipped not just to participate but to lead in this new landscape. This insightful guide by industry leaders Rob Thomas, Paul Zikopoulos, and Kate Soule goes beyond the basics, offering real-life success stories and learned lessons to provide a blueprint for meaningful AI engagement. Whether you're a novice or a seasoned expert, you'll come away with an enhanced understanding of GenAI. Recognize the transformative potential of AI in business and how to harness it Navigate the ethical and operational challenges posed by AI with confidence Understand the dynamic interplay between AI technology and business strategy Implement actionable strategies to integrate AI into your organizational culture Step confidently into the role of an AI value creator, equipped to lead and innovate

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.By working with a single case study throughout this thoroughly revised book, youll learn the entire process of exploratory data analysisfrom collecting data and generating statistics to identifying patterns and testing hypotheses. Youll explore distributions, rules of probability, visualization, and many other tools and concepts.New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.Develop an understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyImport data from most sources with Python, rather than rely on data thats cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

Gaining a Tableau Certified Data Analyst title is key to advertising your skill set in a data-hungry world. It advertises your ability to connect to, analyze, and describe data in an industry-leading data-visualization tool. This study guide provides example questions, terminology, walkthroughs, and best practices to help you learn key topics covered by this certification exam. Christopher Gardner, business intelligence analyst and lead Tableau developer for the University of Michigan, provides guidelines for first-time test-takers and a comprehensive review for Tableau veterans. You'll start by learning how to use Tableau's data Prep Builder and the Tableau Desktop software to connect to, prepare, manipulate, and combine datasets. Then you'll dive into the build interface to develop simple yet compelling data visualizations before moving into more complex topics such as table calculations, level-of-detail expressions, parameters, and predictive analytics tools. This book shows you how to: Read, understand, build, and explain data Manipulate data to prepare it for analysis Combine datasets to expand potential insights Navigate through the Tableau Prep and Tableau Desktop interface to work with your data Set up basic data visualizations and write calculations to perform functions for data enhancement Explore and analyze your data through visualization Publish your work to private or public audiences and properly secure access And much more

In today's data-driven world, organizations must manage and analyze vast amounts of information to deliver the insights that give them a competitive advantage. Many turn to the medallion architecture because it's a proven and well-known design. Yet implementing a robust data pipeline can be difficult, particularly when it comes to using the medallion architecture's bronze, silver, and gold layers--done wrong, it can hamper your ability to make data-driven decisions. This practical guide helps you build a medallion architecture the right way with Azure Databricks and Microsoft Fabric. Drawing on hands-on experience from the field, Piethein Strengholt demystifies common assumptions and complex problems you'll face when embarking on a new data architecture. Architects and engineers of all stripes will find answers to the most typical questions along with insights from real organizations about what's worked, what hasn't, and why. You'll learn: Lakehouse and medallion architecture fundamentals and key concepts Design considerations for Azure Databricks and Microsoft Fabric Scaling considerations, including governance, security, automation, and more How to make informed decisions when designing or implementing new data architectures Proven patterns for success that align with broader organizational objectives

Looking to improve the performance of Cypher queries or learn how to model graphs to support business use cases? A graph database like Neo4j can help. In fact, many enterprises are leveraging Neo4j to power their business-critical applications. This book offers practical and concise recipes on how and when to successfully leverage Neo4j into architectures. Authors Christophe Willemsen and Luanne Misquitta walk you through typical Neo4j implementation strategies from proof of concept to iterative improvements and, finally, to production readiness and beyond. By the end of this book, you should understand how to: Make practical decisions in the proof of concept stage to maximize value Revisit and revise your decisions when transitioning to production Configure and implement observability features for in-production data graphs Integrate graph databases into existing enterprise architectures

Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Pythons implementation. Youll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs.How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations.Get a better grasp of numpy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on a local or remote clusterSolve large problems while using less RAM

Ready to build applications using generative AI? This practical book outlines the process necessary to design and build production grade AI services with a FastAPI web server that communicate seamlessly with databases, payment systems, and external APIs. You'll learn how to develop autonomous generative AI agents that stream outputs in real-time and interact with other models. Web developers, data scientists, and DevOps engineers will learn to implement end-to-end production-ready services that leverage generative AI. You'll learn design patterns to manage software complexity, implement FastAPI lifespan for AI model integration, handle long-running generative tasks, perform content filtering, cache outputs, implement retrieval augmented generation (RAG) with a vector database, implement usage/cost monitoring and tracking, protect services with your own authentication and authorization mechanisms, and effectively control stream outputs directly from GenAI models. You'll explore efficient testing methods for AI outputs, validation against databases, and deployment patterns using Docker for robust microservices in the cloud. Build generative services that interact with databases, external APIs, and more Learn how to load AI models into a FastAPI lifecycle memory Monitor and log model requests and responses within services Use authentication and authorization patterns hooked with generative models Handle and cache long-running inference tasks Stream model outputs via streaming events and WebSockets into browsers or files Automate the retraining process of generative models by exposing event-driven endpoints Ali Parandeh is a Chartered Engineer with the UK Engineering Council and a Microsoft and Google certified developer, data engineer, and data scientist.

If you're developing or considering cloud application architectures for your company's projects, this practical guide is an ideal place to learn and understand best practices for developing in the cloud. Architects and lead developers will learn how cloud applications should be designed, how they fit into a larger architectural picture, and how to make them operate efficiently. Authors Kyle Brown, Bobby Woolf, and Joe Yoder take you through the process step by step. You'll learn: Proven architectural practices for developing applications for the cloud Why some architectural choices are better suited than others for applications intended to run on the cloud How different technical choices work together to make applications better suited for the cloud Design and Implementation techniques that work well for developing cloud applications Ways to select the most appropriate cloud adoption patterns for your organization How all potential choices in application design relate to each other through the connections of the patterns How to chart your own course in adopting the right strategies for developing application architectures for the cloud Kyle Brown is an IBM Fellow, vice president and CTO for the IBM CIO and author of The Cloud Adoption Playbook. Bobby Woolf is an Open Group Certified Distinguished Technical Specialist who works with IBM clients and partners and is coauthor of Enterprise Integration Patterns. Joe Yoder is a distinguished member of the Association for Computing Machinery and a founder and principal of The Refactory. He's coauthor of A Scrum Book: The Spirit of the Game.

Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies are still trying to solve problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more. Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios. Throughout this journey, you'll use open source data tools and public cloud services to see how to put each pattern into practice. You'll learn: Challenges data engineers face and their impact on data systems How these challenges relate to data system components What data engineering patterns are for How to identify and fix issues with your current data components Technology-agnostic solutions to new and existing data projects How to implement patterns with Apache Airflow, Apache Spark, Apache Flink, and Delta Lake Bartosz Konieczny is a freelance data engineer who's been coding for more than 15 years. He's held various senior hands-on positions that helped him work on many data engineering problems in batch and stream processing.

In this practical book, authors John Walsh and Uzi Ailon provide conceptual frameworks, technology overviews, and practical code snippets to help DevSecOps engineers, cybersecurity engineers, security managers, and software developers address use cases across CI/CD pipelines, Kubernetes and cloud native, hybrid and multicloud, and more.

Virtualization, cloud, containers, server automation, and software-defined networking are meant to simplify IT operations. But many organizations adopting these technologies have found that it only leads to a faster-growing sprawl of unmanageable systems. This is where infrastructure as code can help. With this practical guide, author Kief Morris of ThoughtWorks shows you how to effectively use principles, practices, and patterns pioneered through the DevOps movement to manage cloud age infrastructure.Ideal for system administrators, infrastructure engineers, team leads, and architects, this book demonstrates various tools, techniques, and patterns you can use to implement infrastructure as code. In three parts, youll learn about the platforms and tooling involved in creating and configuring infrastructure elements, patterns for using these tools, and practices for making infrastructure as code work in your environment.Examine the pitfalls that organizations fall into when adopting the new generation of infrastructure technologiesUnderstand the capabilities and service models of dynamic infrastructure platformsLearn about tools that provide, provision, and configure core infrastructure resourcesExplore services and tools for managing a dynamic infrastructureLearn specific patterns and practices for provisioning servers, building server templates, and updating running servers

If you're looking to build a production-ready AI application that enables users to "chat" with your company's private data, then you'll need to master LangChain--a premier AI development framework used by global corporations and startups like Zapier, Replit, Databricks, and more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book will show you step-by-step how to build a production-ready AI chatbot trained on your own data. After reading this book, you'll be equipped to: Understand and use the core components of LangChain in your development projects Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external, up-to-date data Develop and deploy AI chatbots that interact intelligently and contextually with users Utilize LangChain Expression Language to create custom, efficient AI operational chains Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Learn the foundations of LLM app development and how they can be used with LangChain

With the growth of cloud native applications, developers increasingly rely on APIs to make everything work. But security often lags behind, making APIs an attractive target for bad actors looking to access valuable business data. OAuth is a popular way to address this issue, but this open standard doesn't provide sufficient guidelines for using API tokens to protect business data. That alone can lead to vulnerabilities and invite data breaches. By using cloud native components in Kubernetes or similar platforms, organizations can implement a scalable, future-proof security architecture for their systems that follows a zero-trust approach to protect business data. You'll access tokens, claims, and token design with an emphasis on an API-first approach. This book takes readers through an end-to-end security architecture that scales to many components in a cloud native environment, while only requiring simple security code in applications and APIs. You'll learn: Why user identity must be part of your cloud native security stack How to integrate user identity into APIs How to externalize security, secure data access, and authenticate clients using OAuth Methods for running security components in a Kubernetes cluster How to use claims to protect business data in APIs How to follow security best practices for client applications and APIs

Organizations are increasingly vulnerable as attack surfaces grow and cyber threats evolve. Addressing these threats is vital, making attack surface management (ASM) essential for security leaders globally. This practical book provides a comprehensive guide to help you master ASM. Cybersecurity engineers, system administrators, and network administrators will explore key components, from networks and cloud systems to human factors. Authors Ron Eddings and MJ Kaufmann offer actionable solutions for newcomers and experts alike, using machine learning and AI techniques. ASM helps you routinely assess digital assets to gain complete insight into vulnerabilities, and potential threats. The process covers all security aspects, from daily operations and threat hunting to vulnerability management and governance. You'll learn: Fundamental ASM concepts, including their role in cybersecurity How to assess and map your organization's attack surface, including digital assets and vulnerabilities Strategies for identifying, classifying, and prioritizing critical assets Attack surfaces types, including each one's unique security challenges How to align technical vulnerabilities with business risks Principles of continuous monitoring and management to maintain a robust security posture Techniques for automating asset discovery, tracking, and categorization Remediation strategies for addressing vulnerabilities, including patching, monitoring, isolation, and containment How to integrate ASM with incident response and continuously improve cybersecurity strategies ASM is more than a strategy--it's a defense mechanism against growing cyber threats. This guide will help you fortify your digital defense.

Learn the essential skills and concepts for working with data in the cloud using Microsoft Azure. With this practical guide, professionals new to data management and Azure will learn how to leverage Azure services such as Azure Cosmos DB, Azure Storage, Azure SQL, and Microsoft Fabric to create, store, process, analyze, and visualize data.

To succeed in AI and data science, you must first master APIs. API skills are essential for AI and data science success. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. Part 1 takes you step-by-step through coding projects to build APIs using Python and FastAPI and deploy them in the cloud. Part 2 teaches you to consume APIs in a data science project using industry-standard tools. And in Part 3, you'll use ChatGPT, the LangChain framework, and other tools to access your APIs with generative AI and large language models (LLMs). As you complete the chapters in the book, you'll be creating a professional online portfolio demonstrating your new skill with APIs, AI, and data science. You'll learn how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Publish a developer portal for your API Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs Author Ryan Day is a data scientist in the financial services industry and an open source developer.

This cookbook shows data engineers, data scientists, data analysts, and cloud architects how to use Dataproc, integrated with Google Cloud, for data lake modernization, ETL, and secure data science at a fraction of the cost.

Learn how to manage Kubernetes clusters and application configurations with Argo CD, the easy-to-use open source GitOps engine. With this practical book, development teams will quickly gain a foundational understanding of Argo CD for deploying and managing containerized applications - without having to be a Kubernetes expert, and without needing full access to the Kubernetes system. With the adoption of Kubernetes, the ability to effectively manage platform configurations has become a paramount concern. Authors Andrew Block from Red Hat and Christian Hernandez from Akuity show you how to apply GitOps practices with Argo CD to manage one or even thousands of Kubernetes environments with confidence. You'll start with a basic understanding of the Argo CD technology and quickly learn how to achieve faster and more secure deployments. With this book, you will: Learn the basics of applying GitOps principles to your Kubernetes environments Use Argo CD to manage Kubernetes configurations as well as the applications you deploy to the platform Manage the configurations of a single Kubernetes cluster or thousands of clusters Deploy Kubernetes resources using tools such as Kustomize and Helm Understand the importance of managing sensitive material and resources

With this book, you'll learn how to integrate OpenAI ChatGPT and Whisper into your MLOps pipeline to create conversational AI applications. This integration will enable you to build powerful chatbots, virtual assistants, and other conversational AI applications.

Skill Seeker is a practical solution for tracking growth and leveling up your skills. What if we could gamify these parts of life and gain experience points for learning or doing something new? Skill Seeker does just that in a choose-your-own-adventure-style goal-setting guide book.

This will be an update to the first book, focusing more on physical computing than on craft, designing and implementing new interfaces that are intended for the human form. This book explains how sensors, microcontrollers, and actuators can be incorporated into clothing to create wearable interactive systems.

This insightful guide takes you through the integration of Tableau Pulse and Einstein Copilot, explaining their roles within the broader Tableau and Salesforce ecosystems. Author Ann Jackson, an esteemed analytics professional, offers a step-by-step exploration of these tools.

Until recently, infrastructure was the backbone of organizations operating software they developed in-house. But now that cloud vendors run the computers, companies can finally bring the benefits of agile custom-centricity to their own developers. Adding product management to infrastructure organizations is now all the rage. But how's that possible when infrastructure is still the operational layer of the company? This practical book guides engineers, managers, product managers, and leaders through the shifts that modern platform-led organizations require. You'll learn what platform engineering is--and isn't--and what benefits and value it brings to developers and teams. You'll understand what it means to approach a platform as a product and learn some of the most common technical and managerial barriers to success. With this book, you'll: Cultivate a platform-as-product, developer-centric mindset Learn what platform engineering teams are and are not Start the process of adopting platform engineering within your organization Discover what it takes to become a product manager for a platform team Understand the challenges that emerge when you scale platforms Automate processes and self-service infrastructure to speed development and improve developer experience Build out, hire, manage, and advocate for a platform team

Data engineers proficient in Databricks are currently in high demand. As organizations gather more data than ever before, skilled data engineers on platforms like Databricks become critical to business success. The Databricks Data Engineer Associate certification is proof that you have a complete understanding of the Databricks platform and its capabilities, as well as the essential skills to effectively execute various data engineering tasks on the platform. In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits. You'll also learn to develop ETL pipelines in both batch and streaming modes. Moreover, you'll discover how to orchestrate data workflows and design dashboards while maintaining data governance. Finally, you'll dive into the finer points of exactly what's on the exam and learn to prepare for it with mock tests. Author Derar Alhussein teaches you not only the fundamental concepts but also provides hands-on exercises to reinforce your understanding. From setting up your Databricks workspace to deploying production pipelines, each chapter is carefully crafted to equip you with the skills needed to master the Databricks Platform. By the end of this book, you'll know everything you need to ace the Databricks Data Engineer Associate certification exam with flying colors, and start your career as a certified data engineer from Databricks! You'll learn how to: Use the Databricks Platform and Delta Lake effectively Perform advanced ETL tasks using Apache Spark SQL Design multi-hop architecture to process data incrementally Build production pipelines using Delta Live Tables and Databricks Jobs Implement data governance using Databricks SQL and Unity Catalog Derar Alhussein is a senior data engineer with a master's degree in data mining. He has over a decade of hands-on experience in software and data projects, including large-scale projects on Databricks. He currently holds eight certifications from Databricks, showcasing his proficiency in the field. Derar is also an experienced instructor, with a proven track record of success in training thousands of data engineers, helping them to develop their skills and obtain professional certifications.

Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. Youll begin at square one, learning how the web and web pages work, and then steadily build from there. By the end of the book, youll have the skills to create a simple site with multicolumn pages that adapt for mobile devices.Each chapter provides exercises to help you learn various techniques and short quizzes to make sure you understand key concepts.This thoroughly revised edition is ideal for students and professionals of all backgrounds and skill levels. It is simple and clear enough for beginners, yet thorough enough to be a useful reference for experienced developers keeping their skills up to date.Build HTML pages with text, links, images, tables, and formsUse style sheets (CSS) for colors, backgrounds, formatting text, page layout, and even simple animation effectsLearn how JavaScript works and why the language is so important in web designCreate and optimize web images so theyll download as quickly as possibleNEW! Use CSS Flexbox and Grid for sophisticated and flexible page layoutNEW! Learn the ins and outs of Responsive Web Design to make web pages look great on all devicesNEW! Become familiar with the command line, Git, and other tools in the modern web developers toolkitNEW! Get to know the super-powers of SVG graphics

Learn to leverage Python for advanced 3D data processing, visualization, and AI-enabled workflows. Define 3D virtual realms, immersive environments, and digital automation to analyze physical objects. With a focus on practicality, this book offers data scientists, engineers, and developers a hands-on approach to working with 3D data. From 3D reconstruction to 3D deep learning techniques, you'll learn how to extract valuable insights from massive datasets, including point clouds, voxels, 3D meshes, images, and more. Author Florent Poux helps you leverage the potential of cutting-edge algorithms and 3D AI models to develop production-ready systems with a focus on automation. You'll learn how to define 3D systems, leverage powerful algorithms, and deploy 3D digital experiences for your industry (geospatial applications, self-driving cars, medical imaging, robotics, manufacturing, computer vision/graphics, and more). This book dives deep into 3D data science with Python: building 3D workflows and wielding Python to implement your solutions. You'll learn: Core concepts and representations of 3D data (meshes, point clouds, voxels, CAD) How to load, manipulate, analyze, and visualize 3D data using powerful Python libraries Advanced AI algorithms for 3D pattern recognition (supervised and unsupervised) 3D reconstruction techniques to generate 3D datasets Automated 3D modeling and generative AI workflows Practical applications of 3D data science in areas like computer vision/graphics, geospatial intelligence, scientific computing, robotics, and autonomous driving How to build end-to-end 3D Python apps through real-world projects Florent Poux is an esteemed authority in the field of 3D data science who teaches and conducts research for top European universities. Heâ s also head professor at the 3D Geodata Academy and innovation director for French Tech 120.

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).