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Spot errors in your Go code you didnGÇÖt even know you were making and boost your productivity by avoiding common mistakes and pitfalls. 100 Go Mistakes: How to Avoid Them shows you how to: Dodge the most common mistakes made by Go developers Structure and organize your Go application Handle data and control structures efficiently Deal with errors in an idiomatic manner Improve your concurrency skills Optimize your code Make your application production-ready and improve testing quality 100 Go Mistakes: How to Avoid Them puts a spotlight on common errors in Go code you might not even know youGÇÖre making. YouGÇÖll explore key areas of the language such as concurrency, testing, data structures, and moreGÇöand learn how to avoid and fix mistakes in your own projects.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, youGÇÖll master scalable techniques for data storage, computation, experiment tracking, and orchestration. YouGÇÖll also learn how to collaborate with data scientists to deliver exactly what they need to succeed. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.

This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.In TensorFlow 2.0 in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. You'll develop a sentiment analyzer for movie reviews, an NLP spam classifier, and other hands-on projects.

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.

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!

An effective DBA is an efficient DBA. And if you work with SQL Server, dbatools is a lifesaver. With over 500 commands, this free and open source PowerShell module has the horsepower to automate just about every task you can imagineGÇöand then some! Written by dbatools creator Chrissy LeMaire and dbatools advocate Rob Sewell, Learn dbatools in a Month of Lunches teaches you techniques that will make you more effectiveGÇöand efficientGÇöthan you ever thought possible.

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!

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!

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!

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.

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.