Machine Learning For Cloud Management

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Machine Learning for Cloud Management

Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan (Of Indian Institute of Technology),Rajkumar Buyya
Machine Learning for Cloud Management Book PDF

Author : Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan (Of Indian Institute of Technology),Rajkumar Buyya
Publisher : Chapman & Hall/CRC
Page : 182 pages
File Size : 43,6 Mb
Release : 2021-11-25
Category : Computers
ISBN : 1000476618

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Machine Learning for Cloud Management by Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan (Of Indian Institute of Technology),Rajkumar Buyya Pdf

Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm. Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms. Key Features: The first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. Predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. It is written by leading international researchers. The book is ideal for researchers who are working in the domain of cloud computing.

Machine Learning for Cloud Management

Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya
Machine Learning for Cloud Management Book PDF

Author : Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya
Publisher : CRC Press
Page : 198 pages
File Size : 53,7 Mb
Release : 2021-11-26
Category : Computers
ISBN : 9781000476590

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Machine Learning for Cloud Management by Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya Pdf

Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm. Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms. Key Features: The first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. Predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain. It is written by leading international researchers. The book is ideal for researchers who are working in the domain of cloud computing.

Machine Learning for Cloud Management

Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya
Machine Learning for Cloud Management Book PDF

Author : Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya
Publisher : CRC Press
Page : 196 pages
File Size : 52,5 Mb
Release : 2021-10
Category : Cloud computing
ISBN : 0367622564

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Machine Learning for Cloud Management by Jitendra Kumar,Ashutosh Kumar Singh,Anand Mohan,Rajkumar Buyya Pdf

Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The book is ideal for researchers who are working in the domain of cloud computing.

Introduction to Machine Learning in the Cloud with Python

Pramod Gupta,Naresh K. Sehgal
Introduction to Machine Learning in the Cloud with Python Book PDF

Author : Pramod Gupta,Naresh K. Sehgal
Publisher : Springer Nature
Page : 284 pages
File Size : 46,7 Mb
Release : 2021-04-28
Category : Technology & Engineering
ISBN : 9783030712709

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Introduction to Machine Learning in the Cloud with Python by Pramod Gupta,Naresh K. Sehgal Pdf

This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure. The authors emphasize fundamentals and best practices for using AI and ML in a dynamic infrastructure with cloud computing and high security, preparing readers to select and make use of appropriate techniques. Important topics are demonstrated using real applications and case studies.

Machine Learning and Optimization Models for Optimization in Cloud

Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar
Machine Learning and Optimization Models for Optimization in Cloud Book PDF

Author : Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar
Publisher : CRC Press
Page : 232 pages
File Size : 48,9 Mb
Release : 2022-02-17
Category : Computers
ISBN : 9781000542257

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Machine Learning and Optimization Models for Optimization in Cloud by Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar Pdf

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Cloud Computing for Machine Learning and Cognitive Applications

Kai Hwang
Cloud Computing for Machine Learning and Cognitive Applications Book PDF

Author : Kai Hwang
Publisher : MIT Press
Page : 626 pages
File Size : 48,9 Mb
Release : 2017-06-16
Category : Computers
ISBN : 9780262036412

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Cloud Computing for Machine Learning and Cognitive Applications by Kai Hwang Pdf

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.

Hands-On Machine Learning on Google Cloud Platform

Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier
Hands On Machine Learning on Google Cloud Platform Book PDF

Author : Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier
Publisher : Packt Publishing Ltd
Page : 489 pages
File Size : 48,7 Mb
Release : 2018-04-30
Category : Computers
ISBN : 9781788398879

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Hands-On Machine Learning on Google Cloud Platform by Giuseppe Ciaburro,V Kishore Ayyadevara,Alexis Perrier Pdf

Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Book Description Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems. What you will learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google’s pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications Who this book is for This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Cloud Computing for Science and Engineering

Ian Foster,Dennis B. Gannon
Cloud Computing for Science and Engineering Book PDF

Author : Ian Foster,Dennis B. Gannon
Publisher : MIT Press
Page : 391 pages
File Size : 52,8 Mb
Release : 2017-09-29
Category : Computers
ISBN : 9780262037242

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Cloud Computing for Science and Engineering by Ian Foster,Dennis B. Gannon Pdf

A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.

Machine Learning Approach for Cloud Data Analytics in IoT

Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
Machine Learning Approach for Cloud Data Analytics in IoT Book PDF

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
Publisher : John Wiley & Sons
Page : 528 pages
File Size : 50,6 Mb
Release : 2021-07-14
Category : Computers
ISBN : 9781119785859

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Machine Learning Approach for Cloud Data Analytics in IoT by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri Pdf

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Machine Learning Empowered Intelligent Data Center Networking

Ting Wang,Bo Li,Mingsong Chen,Shui Yu
Machine Learning Empowered Intelligent Data Center Networking Book PDF

Author : Ting Wang,Bo Li,Mingsong Chen,Shui Yu
Publisher : Springer Nature
Page : 123 pages
File Size : 41,5 Mb
Release : 2023-02-21
Category : Computers
ISBN : 9789811973956

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Machine Learning Empowered Intelligent Data Center Networking by Ting Wang,Bo Li,Mingsong Chen,Shui Yu Pdf

An Introduction to the Machine Learning Empowered Intelligent Data Center Networking Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks. Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security. Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

Pragmatic AI

Noah Gift
Pragmatic AI Book PDF

Author : Noah Gift
Publisher : Addison-Wesley Professional
Page : 720 pages
File Size : 47,8 Mb
Release : 2018-07-12
Category : Computers
ISBN : 9780134863917

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Pragmatic AI by Noah Gift Pdf

Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Artificial Intelligence and Machine Learning in Business Management

Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Artificial Intelligence and Machine Learning in Business Management Book PDF

Author : Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar
Publisher : CRC Press
Page : 243 pages
File Size : 53,6 Mb
Release : 2021-11-05
Category : Business & Economics
ISBN : 9781000432145

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Artificial Intelligence and Machine Learning in Business Management by Sandeep Kumar Panda,Vaibhav Mishra,R. Balamurali,Ahmed A. Elngar Pdf

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

Deep Learning Approaches to Cloud Security

Pramod Singh Rathore,Vishal Dutt,Rashmi Agrawal,Satya Murthy Sasubilli,Srinivasa Rao Swarna
Deep Learning Approaches to Cloud Security Book PDF

Author : Pramod Singh Rathore,Vishal Dutt,Rashmi Agrawal,Satya Murthy Sasubilli,Srinivasa Rao Swarna
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 47,6 Mb
Release : 2022-01-26
Category : Technology & Engineering
ISBN : 9781119760528

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Deep Learning Approaches to Cloud Security by Pramod Singh Rathore,Vishal Dutt,Rashmi Agrawal,Satya Murthy Sasubilli,Srinivasa Rao Swarna Pdf

DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas

Automated Machine Learning

Adnan Masood
Automated Machine Learning Book PDF

Author : Adnan Masood
Publisher : Packt Publishing Ltd
Page : 312 pages
File Size : 42,8 Mb
Release : 2021-02-18
Category : Computers
ISBN : 9781800565524

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Automated Machine Learning by Adnan Masood Pdf

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Intelligent and Cloud Computing

Debahuti Mishra,Rajkumar Buyya,Prasant Mohapatra,Srikanta Patnaik
Intelligent and Cloud Computing Book PDF

Author : Debahuti Mishra,Rajkumar Buyya,Prasant Mohapatra,Srikanta Patnaik
Publisher : Springer Nature
Page : 626 pages
File Size : 52,5 Mb
Release : 2022-04-22
Category : Technology & Engineering
ISBN : 9789811698736

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Intelligent and Cloud Computing by Debahuti Mishra,Rajkumar Buyya,Prasant Mohapatra,Srikanta Patnaik Pdf

This book features a collection of high-quality research papers presented at the International Conference on Intelligent and Cloud Computing (ICICC 2021), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, during October 22–23, 2021. The book includes contributions on system and network design that can support existing and future applications and services. It covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.