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5 books on AI Platforms [PDF]
Updated: May 02, 2024 | 29 |
Books on AI platforms are invaluable resources for startups that aim to provide AI platforms and services. These texts offer insights into the architecture, design, and management of AI platforms, covering topics such as data ingestion, model deployment, scalability, and security. They delve into advanced technologies, including containerization, orchestration, and cloud computing, providing startups with the knowledge necessary to navigate the complexities of building and maintaining robust AI platforms. These books also explore the challenges of user experience, cost management, and data privacy, equipping startups with the insights to address customer needs effectively while adhering to legal and ethical standards.
1. Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models
2023 by Amita Kapoor, Sharmistha Chatterjee
In the book "Platform and Model Design for Responsible AI," readers will gain the expertise to demystify existing opaque machine learning models, rendering them transparent and accountable. The book equips readers with the skills to detect and rectify biases within their models, address uncertainties arising from both data and model limitations, and establish a framework for responsible AI solutions. The journey begins with the creation of ethical models for both traditional and deep learning machine learning systems, followed by their deployment in a sustainable production environment. Readers will also master the art of constructing secure and private data pipelines, validating datasets, and configuring component microservices within a cloud-agnostic framework. Building upon this foundation, the book guides readers in crafting fair and privacy-conscious machine learning models, optimizing hyperparameters, and assessing model performance metrics. By the book's conclusion, readers will possess a thorough understanding of best practices for complying with data privacy and ethical regulations, as well as the essential techniques for data anonymization.
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2. Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment
2021 by Navin Sabharwal, Amit Agrawal
The book "Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment" offers an innovative approach to simplifying the development of AI systems. Within its pages, readers will gain a comprehensive understanding of fundamental concepts in Machine Learning and Natural Language Processing (NLP). Furthermore, the book explores Google's AI services, including AutoML, AI Platform, and Tensorflow, Google's deep learning library, illustrated with practical, real-world examples. It delves into the practical application of AutoML Natural Language services and AI Platform for constructing NLP and Machine Learning models, allowing users to expose their features as REST APIs for use in other applications. Additionally, the book demonstrates the utilization of Google's BigQuery, DataPrep, and DataProc to construct end-to-end machine learning pipelines. Through practical examples such as the Issue Categorization System, Sentiment Analysis, and Loan Default Prediction System, readers will gain profound insights into Google AutoML and AI Platform, equipping them with the knowledge to implement these technologies effectively.
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3. Practical AI on the Google Cloud Platform
2020 by Micheal Lanham
In the book "Practical AI on the Google Cloud Platform," you'll gain proficiency in harnessing the capabilities of Google's AI-driven cloud services for a wide range of applications, including chatbot development and the analysis of text, images, and video data. Authored by Micheal Lanham, this book offers a step-by-step approach to constructing and training models, guiding you in scaling your models to tackle progressively intricate tasks. Assuming a solid foundation in mathematics and proficiency in Python, you'll swiftly familiarize yourself with the Google Cloud Platform, whether your aim is to craft an AI assistant or a straightforward business-oriented AI application. The book covers fundamental concepts in data science, machine learning, and deep learning, and explores tools such as Video AI and AutoML Tables. It walks you through the creation of a basic language processing system employing deep learning techniques, image recognition using Convolutional Neural Networks (CNNs), transfer learning, and Generative Adversarial Networks (GANs). Additionally, you'll delve into the creation of chatbots and conversational AI with Google's Dialogflow, as well as the analysis of video content through automatic indexing, face detection, and TensorFlow Hub. By the book's conclusion, you'll be well-equipped to construct a fully functional AI agent application.
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4. AI as a Service: Serverless machine learning with AWS
2020 by Peter Elger, Eoin Shanaghy
"AI as a Service: Serverless Machine Learning with AWS" is a dynamic and concise handbook that delves into the potential of cloud-based solutions. This resource empowers you to construct practical applications, including chatbots and text-to-speech services, through the integration of cloud elements. The journey commences with modest projects and gradually progresses to the development of substantial, data-driven applications. Key elements encompass applying cloud AI services to existing frameworks, devising and establishing scalable data pipelines, adeptly addressing issues and intricacies within AI services, and expediting your progress with the aid of serverless templates.
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5. Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform
2018 by Mathew Salvaris, Danielle Dean, Wee Hyong Tok
"Explore the world of deep learning with 'Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform,' authored by seasoned data scientists at Microsoft. This book offers practical insights into the intricacies of deep learning on Azure, empowering you to harness its potential for creating innovative and intelligent solutions. Gain valuable guidance on initiating your AI journey and discover how the cloud environment equips you with the necessary tools, infrastructure, and services for AI endeavors. Familiarize yourself with the array of tools, infrastructure, and services available on Microsoft Azure for deep learning, including Azure Machine Learning services and Batch AI. Unlock the capabilities of pre-built AI components like Computer Vision, OCR, gender recognition, emotion analysis, landmark detection, and more. Dive into the realm of common deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), complete with illustrative code samples, while gaining insights into the evolving landscape of the field. Lastly, explore the various options for training and operationalizing deep learning models within the Azure ecosystem."
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How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning models
2023 by Amita Kapoor, Sharmistha Chatterjee
In the book "Platform and Model Design for Responsible AI," readers will gain the expertise to demystify existing opaque machine learning models, rendering them transparent and accountable. The book equips readers with the skills to detect and rectify biases within their models, address uncertainties arising from both data and model limitations, and establish a framework for responsible AI solutions. The journey begins with the creation of ethical models for both traditional and deep learning machine learning systems, followed by their deployment in a sustainable production environment. Readers will also master the art of constructing secure and private data pipelines, validating datasets, and configuring component microservices within a cloud-agnostic framework. Building upon this foundation, the book guides readers in crafting fair and privacy-conscious machine learning models, optimizing hyperparameters, and assessing model performance metrics. By the book's conclusion, readers will possess a thorough understanding of best practices for complying with data privacy and ethical regulations, as well as the essential techniques for data anonymization.
Download PDF
2. Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment
2021 by Navin Sabharwal, Amit Agrawal
The book "Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment" offers an innovative approach to simplifying the development of AI systems. Within its pages, readers will gain a comprehensive understanding of fundamental concepts in Machine Learning and Natural Language Processing (NLP). Furthermore, the book explores Google's AI services, including AutoML, AI Platform, and Tensorflow, Google's deep learning library, illustrated with practical, real-world examples. It delves into the practical application of AutoML Natural Language services and AI Platform for constructing NLP and Machine Learning models, allowing users to expose their features as REST APIs for use in other applications. Additionally, the book demonstrates the utilization of Google's BigQuery, DataPrep, and DataProc to construct end-to-end machine learning pipelines. Through practical examples such as the Issue Categorization System, Sentiment Analysis, and Loan Default Prediction System, readers will gain profound insights into Google AutoML and AI Platform, equipping them with the knowledge to implement these technologies effectively.
Download PDF
3. Practical AI on the Google Cloud Platform
2020 by Micheal Lanham
In the book "Practical AI on the Google Cloud Platform," you'll gain proficiency in harnessing the capabilities of Google's AI-driven cloud services for a wide range of applications, including chatbot development and the analysis of text, images, and video data. Authored by Micheal Lanham, this book offers a step-by-step approach to constructing and training models, guiding you in scaling your models to tackle progressively intricate tasks. Assuming a solid foundation in mathematics and proficiency in Python, you'll swiftly familiarize yourself with the Google Cloud Platform, whether your aim is to craft an AI assistant or a straightforward business-oriented AI application. The book covers fundamental concepts in data science, machine learning, and deep learning, and explores tools such as Video AI and AutoML Tables. It walks you through the creation of a basic language processing system employing deep learning techniques, image recognition using Convolutional Neural Networks (CNNs), transfer learning, and Generative Adversarial Networks (GANs). Additionally, you'll delve into the creation of chatbots and conversational AI with Google's Dialogflow, as well as the analysis of video content through automatic indexing, face detection, and TensorFlow Hub. By the book's conclusion, you'll be well-equipped to construct a fully functional AI agent application.
Download PDF
4. AI as a Service: Serverless machine learning with AWS
2020 by Peter Elger, Eoin Shanaghy
"AI as a Service: Serverless Machine Learning with AWS" is a dynamic and concise handbook that delves into the potential of cloud-based solutions. This resource empowers you to construct practical applications, including chatbots and text-to-speech services, through the integration of cloud elements. The journey commences with modest projects and gradually progresses to the development of substantial, data-driven applications. Key elements encompass applying cloud AI services to existing frameworks, devising and establishing scalable data pipelines, adeptly addressing issues and intricacies within AI services, and expediting your progress with the aid of serverless templates.
Download PDF
5. Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform
2018 by Mathew Salvaris, Danielle Dean, Wee Hyong Tok
"Explore the world of deep learning with 'Deep Learning with Azure: Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform,' authored by seasoned data scientists at Microsoft. This book offers practical insights into the intricacies of deep learning on Azure, empowering you to harness its potential for creating innovative and intelligent solutions. Gain valuable guidance on initiating your AI journey and discover how the cloud environment equips you with the necessary tools, infrastructure, and services for AI endeavors. Familiarize yourself with the array of tools, infrastructure, and services available on Microsoft Azure for deep learning, including Azure Machine Learning services and Batch AI. Unlock the capabilities of pre-built AI components like Computer Vision, OCR, gender recognition, emotion analysis, landmark detection, and more. Dive into the realm of common deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), complete with illustrative code samples, while gaining insights into the evolving landscape of the field. Lastly, explore the various options for training and operationalizing deep learning models within the Azure ecosystem."
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
