The Deep Learning Architects Handbook

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The Deep Learning Architect's Handbook

Author : Ee Kin Chin
Publisher : Packt Publishing Ltd
ISBN 13 : 1803235349
Total Pages : 516 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis The Deep Learning Architect's Handbook by : Ee Kin Chin

Download or read book The Deep Learning Architect's Handbook written by Ee Kin Chin and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

The Machine Learning Solutions Architect Handbook

Author : David Ping
Publisher : Packt Publishing Ltd
ISBN 13 : 1801070415
Total Pages : 442 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis The Machine Learning Solutions Architect Handbook by : David Ping

Download or read book The Machine Learning Solutions Architect Handbook written by David Ping and published by Packt Publishing Ltd. This book was released on 2022-01-21 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build highly secure and scalable machine learning platforms to support the fast-paced adoption of machine learning solutions Key Features Explore different ML tools and frameworks to solve large-scale machine learning challenges in the cloud Build an efficient data science environment for data exploration, model building, and model training Learn how to implement bias detection, privacy, and explainability in ML model development Book DescriptionWhen equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional. What you will learn Apply ML methodologies to solve business problems Design a practical enterprise ML platform architecture Implement MLOps for ML workflow automation Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using an AI service and a custom ML model Use AWS services to detect data and model bias and explain models Who this book is for This book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. You’ll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

Solutions Architect's Handbook

Author : Saurabh Shrivastava
Publisher : Packt Publishing Ltd
ISBN 13 : 183864783X
Total Pages : 475 pages
Book Rating : 4.8/5 (386 download)

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Book Synopsis Solutions Architect's Handbook by : Saurabh Shrivastava

Download or read book Solutions Architect's Handbook written by Saurabh Shrivastava and published by Packt Publishing Ltd. This book was released on 2020-03-21 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: From fundamentals and design patterns to the different strategies for creating secure and reliable architectures in AWS cloud, learn everything you need to become a successful solutions architect Key Features Create solutions and transform business requirements into technical architecture with this practical guide Understand various challenges that you might come across while refactoring or modernizing legacy applications Delve into security automation, DevOps, and validation of solution architecture Book DescriptionBecoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.What you will learn Explore the various roles of a solutions architect and their involvement in the enterprise landscape Approach big data processing, machine learning, and IoT from an architect s perspective and understand how they fit into modern architecture Discover different solution architecture patterns such as event-driven and microservice patterns Find ways to keep yourself updated with new technologies and enhance your skills Modernize legacy applications with the help of cloud integration Get to grips with choosing an appropriate strategy to reduce cost Who this book is for This book is for software developers, system engineers, DevOps engineers, architects, and team leaders working in the information technology industry who aspire to become solutions architect professionals. A good understanding of the software development process and general programming experience with any language will be useful.

Deep Learning for Computer Architects

Author : Brandon Reagen
Publisher : Springer Nature
ISBN 13 : 3031017560
Total Pages : 109 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning for Computer Architects by : Brandon Reagen

Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.

The Machine Learning Solutions Architect Handbook

Author : David Ping
Publisher : Packt Publishing Ltd
ISBN 13 : 180512482X
Total Pages : 603 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis The Machine Learning Solutions Architect Handbook by : David Ping

Download or read book The Machine Learning Solutions Architect Handbook written by David Ping and published by Packt Publishing Ltd. This book was released on 2024-04-15 with total page 603 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design, build, and secure scalable machine learning (ML) systems to solve real-world business problems with Python and AWS Purchase of the print or Kindle book includes a free PDF eBook Key Features Go in-depth into the ML lifecycle, from ideation and data management to deployment and scaling Apply risk management techniques in the ML lifecycle and design architectural patterns for various ML platforms and solutions Understand the generative AI lifecycle, its core technologies, and implementation risks Book DescriptionDavid Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills. You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI. By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.What you will learn Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture Gain an understanding of AI risk management frameworks and techniques Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency Create a business application using artificial intelligence services and custom models Dive into generative AI with use cases, architecture patterns, and RAG Who this book is for This book is for solutions architects working on ML projects, ML engineers transitioning to ML solution architect roles, and MLOps engineers. Additionally, data scientists and analysts who want to enhance their practical knowledge of ML systems engineering, as well as AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management, will also find this book useful. A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook.

The Machine Learning Solutions Architect Handbook

Author : David Ping
Publisher :
ISBN 13 : 9781805122500
Total Pages : 0 pages
Book Rating : 4.1/5 (225 download)

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Book Synopsis The Machine Learning Solutions Architect Handbook by : David Ping

Download or read book The Machine Learning Solutions Architect Handbook written by David Ping and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improve your product knowledge and ownership while building secure and scalable machine learning platformsPurchase of the print or Kindle book includes a free PDF eBook.Key FeaturesSolve large-scale machine learning challenges in the cloud with a variety of open-source and AWS tools and frameworksApply risk management techniques in the machine learning lifecycleUnderstand the key challenges and risks around implementing generative AI and learn architecture patterns for some solutionsBook DescriptionDavid Ping, Head of ML Solutions Architecture at AWS, provides valuable insights and practical examples for becoming a highly skilled ML solutions architect, linking technical architecture to business-related skills.You'll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will focus on carefully selected and updated topics like ML algorithms, including a newly added section on generative AI and large language models. You ll also learn about open-source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines before moving on to building an enterprise ML architecture using Amazon Web Services (AWS).In this latest edition, David has updated the entire book to incorporate the latest advancements in science, technology, and solution patterns. The biggest new addition to the handbook is a comprehensive exploration of ML risk management, generative AI, and a deep understanding of the different stages of AI/ML adoption, allowing you to assess your company's position on its AI/ML journeyBy the end of this book, you will have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, technology, real-world solutions architecture, risk management, governance, and the overall AI/ML journey. Moreover, you will possess the skills to design and construct ML solutions and platforms that effectively cater to common use cases and follow established architecture patterns, enabling you to excel as a true professional in the field.What you will learnApply ML methodologies to solve business problemsDesign a practical enterprise ML platform architectureGain a deep understanding of AI risk management frameworks and techniquesBuild an end-to-end data management architecture using AWSTrain large-scale ML models and optimize model inference latencyCreate a business application using AI services and custom modelsDive into generative AI with use cases, architecture patterns, risks, and ethical considerationsWho this book is forThis book is for data scientists, data engineers, cloud architects, and machine learning enthusiasts who want to become machine learning solutions architects. Also, this book is a great companion for AI/ML product managers and risk officers who want to gain an understanding of ML solutions and AI risk management and AI/ML solutions architects who want to expand their scope of knowledge around AI/ML. You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook.

Software Architect’s Handbook

Author : Joseph Ingeno
Publisher : Packt Publishing Ltd
ISBN 13 : 1788627679
Total Pages : 584 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Software Architect’s Handbook by : Joseph Ingeno

Download or read book Software Architect’s Handbook written by Joseph Ingeno and published by Packt Publishing Ltd. This book was released on 2018-08-30 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to exploring software architecture concepts and implementing best practices Key Features Enhance your skills to grow your career as a software architect Design efficient software architectures using patterns and best practices Learn how software architecture relates to an organization as well as software development methodology Book Description The Software Architect’s Handbook is a comprehensive guide to help developers, architects, and senior programmers advance their career in the software architecture domain. This book takes you through all the important concepts, right from design principles to different considerations at various stages of your career in software architecture. The book begins by covering the fundamentals, benefits, and purpose of software architecture. You will discover how software architecture relates to an organization, followed by identifying its significant quality attributes. Once you have covered the basics, you will explore design patterns, best practices, and paradigms for efficient software development. The book discusses which factors you need to consider for performance and security enhancements. You will learn to write documentation for your architectures and make appropriate decisions when considering DevOps. In addition to this, you will explore how to design legacy applications before understanding how to create software architectures that evolve as the market, business requirements, frameworks, tools, and best practices change over time. By the end of this book, you will not only have studied software architecture concepts but also built the soft skills necessary to grow in this field. What you will learn Design software architectures using patterns and best practices Explore the different considerations for designing software architecture Discover what it takes to continuously improve as a software architect Create loosely coupled systems that can support change Understand DevOps and how it affects software architecture Integrate, refactor, and re-architect legacy applications Who this book is for The Software Architect’s Handbook is for you if you are a software architect, chief technical officer (CTO), or senior developer looking to gain a firm grasp of software architecture.

Big Data Architect’s Handbook

Author : Syed Muhammad Fahad Akhtar
Publisher : Packt Publishing Ltd
ISBN 13 : 1788836383
Total Pages : 476 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Big Data Architect’s Handbook by : Syed Muhammad Fahad Akhtar

Download or read book Big Data Architect’s Handbook written by Syed Muhammad Fahad Akhtar and published by Packt Publishing Ltd. This book was released on 2018-06-21 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architects Who this book is for Big Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.

Architects of Intelligence

Author : Martin Ford
Publisher : Packt Publishing Ltd
ISBN 13 : 178913126X
Total Pages : 540 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Architects of Intelligence by : Martin Ford

Download or read book Architects of Intelligence written by Martin Ford and published by Packt Publishing Ltd. This book was released on 2018-11-23 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Times Best Books of the Year 2018 TechRepublic Top Books Every Techie Should Read Book Description How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future. Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.

Architecture in the Age of Artificial Intelligence

Author : Neil Leach
Publisher : Bloomsbury Publishing
ISBN 13 : 1350165549
Total Pages : 281 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Architecture in the Age of Artificial Intelligence by : Neil Leach

Download or read book Architecture in the Age of Artificial Intelligence written by Neil Leach and published by Bloomsbury Publishing. This book was released on 2021-11-18 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence is everywhere – from the apps on our phones to the algorithms of search engines. Without us noticing, the AI revolution has arrived. But what does this mean for the world of design? The first volume in a two-book series, Architecture in the Age of Artificial Intelligence introduces AI for designers and considers its positive potential for the future of architecture and design. Explaining what AI is and how it works, the book examines how different manifestations of AI will impact the discipline and profession of architecture. Highlighting current case-studies as well as near-future applications, it shows how AI is already being used as a powerful design tool, and how AI-driven information systems will soon transform the design of buildings and cities. Far-sighted, provocative and challenging, yet rooted in careful research and cautious speculation, this book, written by architect and theorist Neil Leach, is a must-read for all architects and designers – including students of architecture and all design professionals interested in keeping their practice at the cutting edge of technology.

Enterprise DevOps for Architects

Author : Jeroen Mulder
Publisher : Packt Publishing Ltd
ISBN 13 : 1801811709
Total Pages : 289 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Enterprise DevOps for Architects by : Jeroen Mulder

Download or read book Enterprise DevOps for Architects written by Jeroen Mulder and published by Packt Publishing Ltd. This book was released on 2021-11-11 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: An architect's guide to designing, implementing, and integrating DevOps in the enterprise Key FeaturesDesign a DevOps architecture that is aligned with the overall enterprise architectureDesign systems that are ready for AIOps and make the move toward NoOpsArchitect and implement DevSecOps pipelines, securing the DevOps enterpriseBook Description Digital transformation is the new paradigm in enterprises, but the big question remains: is the enterprise ready for transformation using native technology embedded in Agile/DevOps? With this book, you'll see how to design, implement, and integrate DevOps in the enterprise architecture while keeping the Ops team on board and remaining resilient. The focus of the book is not to introduce the hundreds of different tools that are available for implementing DevOps, but instead to show you how to create a successful DevOps architecture. This book provides an architectural overview of DevOps, AIOps, and DevSecOps – the three domains that drive and accelerate digital transformation. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this DevOps book will help you to successfully integrate DevOps into enterprise architecture. You'll learn what AIOps is and what value it can bring to an enterprise. Lastly, you will learn how to integrate security principles such as zero-trust and industry security frameworks into DevOps with DevSecOps. By the end of this DevOps book, you'll be able to develop robust DevOps architectures, know which toolsets you can use for your DevOps implementation, and have a deeper understanding of next-level DevOps by implementing Site Reliability Engineering (SRE). What you will learnCreate DevOps architecture and integrate it with the enterprise architectureDiscover how DevOps can add value to the quality of IT deliveryExplore strategies to scale DevOps for an enterpriseArchitect SRE for an enterprise as next-level DevOpsUnderstand AIOps and what value it can bring to an enterpriseCreate your AIOps architecture and integrate it into DevOpsCreate your DevSecOps architecture and integrate it with the existing DevOps setupApply zero-trust principles and industry security frameworks to DevOpsWho this book is for This book is for enterprise architects and consultants who want to design DevOps systems for the enterprise. It provides an architectural overview of DevOps, AIOps, and DevSecOps. If you're looking to learn about the implementation of various tools within the DevOps toolchain in detail, this book is not for you.

Building Machine Learning and Deep Learning Models on Google Cloud Platform

Author : Ekaba Bisong
Publisher : Apress
ISBN 13 : 1484244702
Total Pages : 703 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Building Machine Learning and Deep Learning Models on Google Cloud Platform by : Ekaba Bisong

Download or read book Building Machine Learning and Deep Learning Models on Google Cloud Platform written by Ekaba Bisong and published by Apress. This book was released on 2019-09-27 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

PyTorch 1.x Reinforcement Learning Cookbook

Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
ISBN 13 : 1838553231
Total Pages : 334 pages
Book Rating : 4.8/5 (385 download)

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Book Synopsis PyTorch 1.x Reinforcement Learning Cookbook by : Yuxi (Hayden) Liu

Download or read book PyTorch 1.x Reinforcement Learning Cookbook written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2019-10-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.

Icle

Author : Lissa Pijanowski
Publisher : Ntl Ctr Leadership in Education
ISBN 13 : 9781328017628
Total Pages : 0 pages
Book Rating : 4.0/5 (176 download)

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Book Synopsis Icle by : Lissa Pijanowski

Download or read book Icle written by Lissa Pijanowski and published by Ntl Ctr Leadership in Education. This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a Learning Architect and Modernize Instructional Practice What does it take to transform into a master educator in today's age? How does one become a true architect of deeper learning, designing a blueprint for building successful student futures? Educator, advocate, and author Lissa Pijanowski's groundbreaking Architects of Deeper Learning shows educators how to design, implement, and assess meaningful classroom work, thereby enabling students to solve complex problems, communicate effectively, collaborate with others, and generate powerful ideas. Incorporating insights gleaned from extensive study and years of classroom experience, Pijanowski presents an innovative philosophy of teaching and leadership. Based on eight design principles to facilitate deeper learning, the Blueprint for Deeper Learning offers a clear framework for designing student learning experiences that are rigorous, relevant, and engaging. This blueprint is an anchor for all content areas across K-12 and is compatible with any set of standards. Architects of Deeper Learning equips educators with the tools they need to modernize instructional practice. Featuring a step-by-step overview of the lesson-building process--from setting learning goals to assessing student work--Pijanowski reveals how to craft lessons that best prepare students to be future-ready. Ready to redesign your instruction and turn good into great? Begin your journey to become an architect of deeper learning today.

Salesforce Architect's Handbook

Author : Dipanker Jyoti
Publisher : Apress
ISBN 13 : 9781484266304
Total Pages : 430 pages
Book Rating : 4.2/5 (663 download)

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Book Synopsis Salesforce Architect's Handbook by : Dipanker Jyoti

Download or read book Salesforce Architect's Handbook written by Dipanker Jyoti and published by Apress. This book was released on 2021-01-08 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a deep dive into the architectural approach, best practices, and key considerations needed to comprehend, evaluate, and design an efficient, scalable, and sustainable Salesforce-based solution. This book takes a comprehensive look at the seven architectural domains that must be considered when architecting a Salesforce-based solution and equips you to develop the artifacts needed for an end-to-end enterprise architecture blueprint for Salesforce implementation and DevOps. This must-have handbook helps Salesforce professionals implement and manage Salesforce in their organization. You will learn Salesforce architecture: solution architecture, data architecture, security architecture, integration architecture, identity and access management architecture, and strategies that can be used for Salesforce-based mobile applications. In addition to the main architecture concepts, the book also offers industry best practices and the recommended framework for approaching, managing, delivering, and continuously improving a Salesforce solution using its Salesforce Development & Deployment Lifecycle. What You Will Learn Get a detailed overview of the Salesforce multi-tenant, metadata-driven architectural framework and the under-the-hood technology stack that supports Salesforce Know the seven architecture domains, their intricacies, and the considerations needed within each when designing a Salesforce solution Have an architectural mindset and the artifacts needed to architect an end-to-end enterprise-level implementation of Salesforce Be familiar with the most common Salesforce products, licenses, AppExchange products, and the key considerations of using out-of-the-box declarative capabilities vs custom programmatic capabilities of Salesforce Understand data architecture design considerations that include data modeling in Salesforce, identifying and mitigating large data volume concerns, and the key considerations for data migration and data archiving strategies Understand security architecture considerations related to securing data within Salesforce and the various approaches to allow or restrict sharing and visibility from within Salesforce Understand integration architecture considerations that provide an overview of the integration patterns and the integrations solutions that can be used with Salesforce to connect Salesforce with a remote system hosted on-premises, on the cloud, or by third-party solution providers Understand identity and access management architectural considerations across the 9 stages of an identity and access management lifecycle Be aware of the strategies available to design mobile solutions with Salesforce and the options available for Salesforce mobile architecture Employ the principles of the DevOps & Development Lifecycle needed for an ideal state Salesforce implementation Who This Book Is For Professionals interested in implementing, optimizing, and architecting Salesforce solutions enterprise-wide; Salesforce implementation (SI) partners needing a detailed playbook for architecting and delivering successful Salesforce solutions; Salesforce admins, developers, and architects looking for a one-stop educational resource to mastering the Salesforce architect domains or those pursuing the Salesforce architecture domain certification exams, including the Salesforce Certified Technical Architect (CTA) board exam

Machine Learning

Author : Phil Bernstein
Publisher : Routledge
ISBN 13 : 1000600688
Total Pages : 173 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Machine Learning by : Phil Bernstein

Download or read book Machine Learning written by Phil Bernstein and published by Routledge. This book was released on 2022-04-30 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: ‘The advent of machine learning-based AI systems demands that our industry does not just share toys, but builds a new sandbox in which to play with them.’ - Phil Bernstein The profession is changing. A new era is rapidly approaching when computers will not merely be instruments for data creation, manipulation and management, but, empowered by artificial intelligence, they will become agents of design themselves. Architects need a strategy for facing the opportunities and threats of these emergent capabilities or risk being left behind. Architecture’s best-known technologist, Phil Bernstein, provides that strategy. Divided into three key sections – Process, Relationships and Results – Machine Learning lays out an approach for anticipating, understanding and managing a world in which computers often augment, but may well also supplant, knowledge workers like architects. Armed with this insight, practices can take full advantage of the new technologies to future-proof their business. Features chapters on: Professionalism Tools and technologies Laws, policy and risk Delivery, means and methods Creating, consuming and curating data Value propositions and business models.

Deep Learning Systems

Author : Andres Rodriguez
Publisher : Springer Nature
ISBN 13 : 3031017692
Total Pages : 245 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Learning Systems by : Andres Rodriguez

Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.