Machine Learning Infrastructure And Best Practices For Software Engineers
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Machine Learning Infrastructure And Best Practices For Software Engineers
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Author : Miroslaw Staron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Machine Learning Infrastructure And Best Practices For Software Engineers written by Miroslaw Staron and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.
A Guide To Implementing Mlops
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Author : Prafful Mishra
language : en
Publisher: Springer Nature
Release Date : 2025-02-01
A Guide To Implementing Mlops written by Prafful Mishra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-01 with Computers categories.
Over the past decade, machine learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of machine learning, many organisations need help deploying and managing machine learning models in production. This is where MLOps comes in. MLOps, or machine learning operations, is an emerging field that focuses on the deployment, management, and monitoring of machine learning models in production environments. MLOps combines the principles of DevOps with the unique requirements of machine learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations. The book is aimed at data scientists, machine learning engineers, and IT professionals who are interested in deploying machine learning models at scale. It assumes a basic understanding of machine learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling machine learning in production.
Machine Learning For Software Quality And Reliability Transforming Software Engineering
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Author : Hariprasad Sivaraman
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2020-11-13
Machine Learning For Software Quality And Reliability Transforming Software Engineering written by Hariprasad Sivaraman and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
As software systems grow in complexity, ensuring quality, reliability, and efficiency has become more challenging than ever. Machine Learning for Software Quality and Reliability explores how AI-driven techniques are revolutionizing software engineering, enhancing defect prediction, automating testing, and optimizing performance. This book provides a deep dive into machine learning applications across the software development lifecycle. Readers will discover how ML models improve defect detection, automate test case generation, and enhance predictive maintenance, reducing downtime and increasing system stability. Key topics include supervised and unsupervised learning for software quality, anomaly detection for reliability engineering, and ML- powered root cause analysis. By integrating real-world case studies from leading technology companies, the book demonstrates how AI- driven quality assurance improves efficiency and scalability across industries. Learn how Google, Microsoft, and Netflix leverage ML to enhance software reliability, minimize bugs, and optimize continuous integration/continuous deployment (CI/CD) pipelines. With a focus on practical implementation, Machine Learning for Software Quality and Reliability equips software engineers, IT leaders, and quality assurance professionals with actionable insights to integrate ML into their workflows. Whether you ' re seeking to automate testing, enhance system resilience, or improve software reliability, this book provides the roadmap to harnessing AI for the next generation of high-quality software development.
Data Science For Decision Makers
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Author : Jon Howells
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26
Data Science For Decision Makers written by Jon Howells and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Computers categories.
Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key Features Master the concepts of statistics and ML to interpret models and guide decisions Identify valuable AI use cases and manage data science projects from start to finish Empower top data science teams to solve complex problems and build AI products Purchase of the print Kindle book includes a free PDF eBook Book DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.What you will learn Discover how to interpret common statistical quantities and make data-driven decisions Explore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learning Find out how to evaluate statistical and machine learning models Understand the data science lifecycle, from development to monitoring of models in production Know when to use ML, statistical modeling, or traditional BI methods Manage data teams and data science projects effectively Who this book is for This book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.
Machine Learning Governance For Managers
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Author : Francesca Lazzeri
language : en
Publisher: Springer Nature
Release Date : 2023-11-24
Machine Learning Governance For Managers written by Francesca Lazzeri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-24 with Mathematics categories.
Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance. Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized. Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.
Infinite Loops Navigating The Landscape Of Software Engineering
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Author : Sumit Jha
language : en
Publisher: Skillworldhub
Release Date : 2024-05-18
Infinite Loops Navigating The Landscape Of Software Engineering written by Sumit Jha and has been published by Skillworldhub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-18 with Computers categories.
Embark on a journey through the dynamic world of software engineering with "Infinite Loops." This comprehensive guide delves into the essential principles, practices, and emerging trends shaping the field of software engineering today. From understanding user needs and designing robust architectures to ensuring software security and exploring the frontiers of AI and quantum computing, this book covers it all. Whether you're a seasoned software professional or a curious newcomer, "Infinite Loops" offers invaluable insights and practical guidance to navigate the ever-changing landscape of software engineering and stay ahead in the digital age.
Software Engineering At Google
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Author : Titus Winters
language : en
Publisher: O'Reilly Media
Release Date : 2020-02-28
Software Engineering At Google written by Titus Winters and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-28 with Computers categories.
Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life? Based on their experience at Google, software engineers Titus Winters and Hyrum Wright, along with technical writer Tom Manshreck, present a candid and insightful look at how some of the worldâ??s leading practitioners construct and maintain software. This book covers Googleâ??s unique engineering culture, processes, and tools and how these aspects contribute to the effectiveness of an engineering organization. Youâ??ll explore three fundamental principles that software organizations should keep in mind when designing, architecting, writing, and maintaining code: How time affects the sustainability of software and how to make your code resilient over time How scale affects the viability of software practices within an engineering organization What trade-offs a typical engineer needs to make when evaluating design and development decisions
Real Life Infrastructure As Code With Aws Cdk
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Author : Andre Sionek
language : en
Publisher: Andre Sionek
Release Date : 2025-01-01
Real Life Infrastructure As Code With Aws Cdk written by Andre Sionek and has been published by Andre Sionek this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Computers categories.
Dive into the world of Infrastructure as Code (IaC) with 'Real-Life Infrastructure as Code with AWS CDK'. Perfect for developers and data engineers, this guide offers practical examples, best practices, and expert insights into building and managing cloud infrastructure using AWS CDK. Whether you're looking to streamline deployments, enhance scalability, or secure your cloud environments, this book equips you with the knowledge to leverage IaC principles effectively. Transform your development workflow and bring your projects from concept to production. This book will show you how to build a modern software platform in Python using AWS CDK. Even if you use a different language, you will find this book useful because I focus on architecture patterns rather than syntax details. The book is divided into three parts: Foundations, Real-Life Examples, and Best Practices. begin with an introduction to IaC and CDK to help you quickly learn and refresh some concepts. Then, we dive into a series of real-life implementations of various services and components that you can use to build your software platform. All examples are complete and fully functional, as I have personally deployed them. Finally, I discuss some best practices that I have learned from experience and implemented in the examples. You'll learn: * AWS CDK and IaC concepts. * Cloud computing concepts and services, including the AWS Well-Architected Framework. * How to build a cloud-native software platform using CDK. * Create functional constructs to build your cloud application. * How to create a microservices architecture with CDK.
Big Book Of Blog Niches
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Author : Dennis DeLaurier
language : en
Publisher: Dennis DeLaurier
Release Date : 2024-03-24
Big Book Of Blog Niches written by Dennis DeLaurier and has been published by Dennis DeLaurier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-24 with Computers categories.
About If you are an old or new Blogger, you may be looking for ideas for your next Blog. If you are a new Blogger there is always that Brain Freeze that comes with starting. Below are a BIG BUNCH of some ideas that may help. I hope all the ideas below are helpful Dennis DeLaurier Author
Machine Learning Algorithms And Techniques
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Author : Krishna Bonagiri
language : en
Publisher: RK Publication
Release Date : 2024-06-21
Machine Learning Algorithms And Techniques written by Krishna Bonagiri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-21 with Computers categories.
Machine Learning Algorithms and Techniques the concepts, popular algorithms, and essential techniques of machine learning. A comprehensive covering supervised, unsupervised, and reinforcement learning methods while exploring key algorithms like decision trees, neural networks, clustering, and more. Practical applications and examples bring each algorithm to life, helping readers understand how these models are used to solve real-world problems. Designed for both beginners and experienced practitioners, this book is an ideal guide for mastering the fundamentals and applications of machine learning.