Architecting Data And Machine Learning Platforms

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Architecting Data and Machine Learning Platforms

Author : Marco Tranquillin
Publisher :
ISBN 13 : 9781098151614
Total Pages : 0 pages
Book Rating : 4.1/5 (516 download)

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Book Synopsis Architecting Data and Machine Learning Platforms by : Marco Tranquillin

Download or read book Architecting Data and Machine Learning Platforms written by Marco Tranquillin and published by . This book was released on 2024-01-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: All cloud architects need to know how to build data platforms--the key to enabling businesses with data and delivering enterprise-wide intelligence in a fast and efficient way. This handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle in a cloud environment, from ingestion to activation, using real-world enterprise architectures. You'll learn how to transform and modernize familiar solutions, like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. This book shows you how to: Design a modern cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a data platform Democratize access to enterprise data and allow business teams to extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Move from a descriptive analytics approach to a more predictive and prescriptive one by building an MLOps platform Make your organization more effective in working with data analytics and machine learning in a cloud environment

Architecting Data and Machine Learning Platforms

Author : Marco Tranquillin
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098151585
Total Pages : 361 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Architecting Data and Machine Learning Platforms by : Marco Tranquillin

Download or read book Architecting Data and Machine Learning Platforms written by Marco Tranquillin and published by "O'Reilly Media, Inc.". This book was released on 2023-10-12 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how to: Design a modern and secure cloud native or hybrid data analytics and machine learning platform Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities Enable your business to make decisions in real time using streaming pipelines Build an MLOps platform to move to a predictive and prescriptive analytics approach

Architecting Modern Data Platforms

Author : Jan Kunigk
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491969229
Total Pages : 636 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Architecting Modern Data Platforms by : Jan Kunigk

Download or read book Architecting Modern Data Platforms written by Jan Kunigk and published by "O'Reilly Media, Inc.". This book was released on 2018-12-05 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

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.

Designing Cloud Data Platforms

Author : Danil Zburivsky
Publisher : Simon and Schuster
ISBN 13 : 1617296449
Total Pages : 334 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Designing Cloud Data Platforms by : Danil Zburivsky

Download or read book Designing Cloud Data Platforms written by Danil Zburivsky and published by Simon and Schuster. This book was released on 2021-04-20 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.

Machine Learning Design Patterns

Author : Valliappa Lakshmanan
Publisher : O'Reilly Media
ISBN 13 : 1098115759
Total Pages : 408 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Machine Learning Design Patterns by : Valliappa Lakshmanan

Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

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.

Foundations for Architecting Data Solutions

Author : Ted Malaska
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492038695
Total Pages : 196 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Foundations for Architecting Data Solutions by : Ted Malaska

Download or read book Foundations for Architecting Data Solutions written by Ted Malaska and published by "O'Reilly Media, Inc.". This book was released on 2018-08-29 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect

Data Lakehouse in Action

Author : Pradeep Menon
Publisher : Packt Publishing Ltd
ISBN 13 : 1801815100
Total Pages : 206 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Data Lakehouse in Action by : Pradeep Menon

Download or read book Data Lakehouse in Action written by Pradeep Menon and published by Packt Publishing Ltd. This book was released on 2022-03-17 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patterns Key FeaturesUnderstand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success. The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application. By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner. What you will learnUnderstand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.

Machine Learning with Microsoft Technologies

Author : Leila Etaati
Publisher : Apress
ISBN 13 : 1484236580
Total Pages : 363 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning with Microsoft Technologies by : Leila Etaati

Download or read book Machine Learning with Microsoft Technologies written by Leila Etaati and published by Apress. This book was released on 2019-06-12 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn Choose the right Microsoft product for your machine learning solutionCreate and manage Microsoft’s tool environments for development, testing, and production of a machine learning projectImplement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

Azure Data and AI Architect Handbook

Author : Olivier Mertens
Publisher : Packt Publishing Ltd
ISBN 13 : 1803230738
Total Pages : 284 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Azure Data and AI Architect Handbook by : Olivier Mertens

Download or read book Azure Data and AI Architect Handbook written by Olivier Mertens and published by Packt Publishing Ltd. This book was released on 2023-07-31 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions Purchase of the print or Kindle book includes a free PDF eBook Key Features Translate and implement conceptual architectures with the right Azure services Inject artificial intelligence into data solutions for advanced analytics Leverage cloud computing and frameworks to drive data science workloads Book DescriptionWith data’s growing importance in businesses, the need for cloud data and AI architects has never been higher. The Azure Data and AI Architect Handbook is designed to assist any data professional or academic looking to advance their cloud data platform designing skills. This book will help you understand all the individual components of an end-to-end data architecture and how to piece them together into a scalable and robust solution. You’ll begin by getting to grips with core data architecture design concepts and Azure Data & AI services, before exploring cloud landing zones and best practices for building up an enterprise-scale data platform from scratch. Next, you’ll take a deep dive into various data domains such as data engineering, business intelligence, data science, and data governance. As you advance, you’ll cover topics ranging from learning different methods of ingesting data into the cloud to designing the right data warehousing solution, managing large-scale data transformations, extracting valuable insights, and learning how to leverage cloud computing to drive advanced analytical workloads. Finally, you’ll discover how to add data governance, compliance, and security to solutions. By the end of this book, you’ll have gained the expertise needed to become a well-rounded Azure Data & AI architect.What you will learn Design scalable and cost-effective cloud data platforms on Microsoft Azure Explore architectural design patterns with various use cases Determine the right data stores and data warehouse solutions Discover best practices for data orchestration and transformation Help end users to visualize data using interactive dashboarding Leverage OpenAI and custom ML models for advanced analytics Manage security, compliance, and governance for the data estate Who this book is forThis book is for anyone looking to elevate their skill set to the level of an architect. Data engineers, data scientists, business intelligence developers, and database administrators who want to learn how to design end-to-end data solutions and get a bird’s-eye view of the entire data platform will find this book useful. Although not required, basic knowledge of databases and data engineering workloads is recommended.

Architecting the Cloud

Author : Michael J. Kavis
Publisher : John Wiley & Sons
ISBN 13 : 1118826469
Total Pages : 230 pages
Book Rating : 4.1/5 (188 download)

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Book Synopsis Architecting the Cloud by : Michael J. Kavis

Download or read book Architecting the Cloud written by Michael J. Kavis and published by John Wiley & Sons. This book was released on 2014-01-28 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: An expert guide to selecting the right cloud service model for your business Cloud computing is all the rage, allowing for the delivery of computing and storage capacity to a diverse community of end-recipients. However, before you can decide on a cloud model, you need to determine what the ideal cloud service model is for your business. Helping you cut through all the haze, Architecting the Cloud is vendor neutral and guides you in making one of the most critical technology decisions that you will face: selecting the right cloud service model(s) based on a combination of both business and technology requirements. Guides corporations through key cloud design considerations Discusses the pros and cons of each cloud service model Highlights major design considerations in areas such as security, data privacy, logging, data storage, SLA monitoring, and more Clearly defines the services cloud providers offer for each service model and the cloud services IT must provide Arming you with the information you need to choose the right cloud service provider, Architecting the Cloud is a comprehensive guide covering everything you need to be aware of in selecting the right cloud service model for you.

Building Cloud Data Platforms Solutions

Author : Anouar BEN ZAHRA
Publisher : Anouar BEN ZAHRA
ISBN 13 :
Total Pages : 339 pages
Book Rating : 4./5 ( download)

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Book Synopsis Building Cloud Data Platforms Solutions by : Anouar BEN ZAHRA

Download or read book Building Cloud Data Platforms Solutions written by Anouar BEN ZAHRA and published by Anouar BEN ZAHRA. This book was released on with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.

Designing Deep Learning Systems

Author : Chi Wang
Publisher : Simon and Schuster
ISBN 13 : 1638352151
Total Pages : 358 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Designing Deep Learning Systems by : Chi Wang

Download or read book Designing Deep Learning Systems written by Chi Wang and published by Simon and Schuster. This book was released on 2023-09-19 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a deep learning engineer. About the technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms. What's inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the author Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Table of Contents 1 An introduction to deep learning systems 2 Dataset management service 3 Model training service 4 Distributed training 5 Hyperparameter optimization service 6 Model serving design 7 Model serving in practice 8 Metadata and artifact store 9 Workflow orchestration 10 Path to production

Architecting Google Cloud Solutions

Author : Victor Dantas
Publisher : Packt Publishing Ltd
ISBN 13 : 1800564155
Total Pages : 472 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Architecting Google Cloud Solutions by : Victor Dantas

Download or read book Architecting Google Cloud Solutions written by Victor Dantas and published by Packt Publishing Ltd. This book was released on 2021-05-14 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Achieve your business goals and build highly available, scalable, and secure cloud infrastructure by designing robust and cost-effective solutions as a Google Cloud Architect. Key FeaturesGain hands-on experience in designing and managing high-performance cloud solutionsLeverage Google Cloud Platform to optimize technical and business processes using cutting-edge technologies and servicesUse Google Cloud Big Data, AI, and ML services to design scalable and intelligent data solutionsBook Description Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs. You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance. By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform. What you will learnGet to grips with compute, storage, networking, data analytics, and pricingDiscover delivery models such as IaaS, PaaS, and SaaSExplore the underlying technologies and economics of cloud computingDesign for scalability, business continuity, observability, and resiliencySecure Google Cloud solutions and ensure complianceUnderstand operational best practices and learn how to architect a monitoring solutionGain insights into modern application design with Google CloudLeverage big data, machine learning, and AI with Google CloudWho this book is for This book is for cloud architects who are responsible for designing and managing cloud solutions with GCP. You'll also find the book useful if you're a system engineer or enterprise architect looking to learn how to design solutions with Google Cloud. Moreover, cloud architects who already have experience with other cloud providers and are now beginning to work with Google Cloud will benefit from the book. Although an intermediate-level understanding of cloud computing and distributed apps is required, prior experience of working in the public and hybrid cloud domain is not mandatory.

Practitioner’s Guide to Data Science

Author : Nasir Ali Mirza
Publisher : BPB Publications
ISBN 13 : 9391392873
Total Pages : 273 pages
Book Rating : 4.3/5 (913 download)

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Book Synopsis Practitioner’s Guide to Data Science by : Nasir Ali Mirza

Download or read book Practitioner’s Guide to Data Science written by Nasir Ali Mirza and published by BPB Publications. This book was released on 2022-01-17 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers Data Science concepts, processes, and the real-world hands-on use cases. KEY FEATURES ● Covers the journey from a basic programmer to an effective Data Science developer. ● Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ● Implementation of MLOps using Microsoft Azure DevOps. DESCRIPTION "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. WHAT YOU WILL LEARN ● Organize Data Science projects using CRISP-DM and Microsoft TDSP. ● Learn to acquire and explore data using Python visualizations. ● Get well versed with the implementation of data pre-processing and Feature Engineering. ● Understand algorithm selection, model development, and model evaluation. ● Hands-on with Azure ML Service, its architecture, and capabilities. ● Learn to use Azure ML SDK and MLOps for implementing real-world use cases. WHO THIS BOOK IS FOR This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. TABLE OF CONTENTS 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models

Software Architecture for Big Data and the Cloud

Author : Ivan Mistrik
Publisher : Morgan Kaufmann
ISBN 13 : 0128093382
Total Pages : 472 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Software Architecture for Big Data and the Cloud by : Ivan Mistrik

Download or read book Software Architecture for Big Data and the Cloud written by Ivan Mistrik and published by Morgan Kaufmann. This book was released on 2017-06-12 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data