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5 books on AI Platforms [PDF]
October 24, 2024 | 29 |
These books offer insights into structure and capabilities of AI platforms, covering topics such as cognitive API, data ingestion, model deployment, scalability and security.
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 this book, Amita Kapoor and Sharmistha Chatterjee invite you on a noble quest to turn mysterious, black-box machine learning models into upstanding, transparent citizens. Learn to sniff out and eliminate bias, secure your data pipelines and wrangle the tangled jungle of microservices—all while keeping your models fair, resilient and fully clothed in ethical considerations. By the end, you’ll be constructing models so virtuous they could pass an ethics exam, all while navigating the complex waters of data privacy with the grace of an AI-powered swan.
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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
Navin Sabharwal and Amit Agrawal take you on a whimsical journey through Google's magical AI forest, where AutoML fairies and AI Platform gnomes help even the greenest traveler create production-ready AI. With charming examples like predicting loan defaults and categorizing issues, readers will become fluent in Googlish tools like BigQuery, DataPrep and DataProc. Armed with REST APIs and boundless enthusiasm, you'll find that conjuring NLP and machine learning models from thin air isn’t just for Silicon Valley sorcerers anymore.
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3. Practical AI on the Google Cloud Platform
2020 by Micheal Lanham
In Micheal Lanham's adventurous how-to manual, you’ll learn that building AI on Google’s Cloud is kind of like assembling a cosmic LEGO set—only these blocks let you create chatbots, analyze images and index video content. Whether you want an AI assistant to order your coffee or help your business sort through mountains of data, this book’s got you covered. With deep dives into Google’s bag of tricks—from Dialogflow to AutoML Tables—you’ll soon be a bona fide AI wizard, casting models and scaling them to tackle feats both great and small.
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4. AI as a Service: Serverless machine learning with AWS
2020 by Peter Elger, Eoin Shanaghy
Peter Elger and Eoin Shanaghy present a lively romp through the world of serverless AI with Amazon’s wizard-like cloud infrastructure. Ever wonder how to make a chatbot from virtually nothing? Or have a voice assistant serenade you with data-driven wisdom? This book starts you off with humble projects, letting you slowly transform into a serverless AI conjurer. With an arsenal of serverless templates at your side, you’ll be scaling data pipelines and tackling AI service headaches faster than you can say “cloud magic.”
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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
Mathew Salvaris, Danielle Dean and Wee Hyong Tok invite you to Microsoft’s playground of deep learning wonders, Azure. This is your chance to learn how to wield the cloud to conjure up AI marvels—think of it as an enchanted workshop full of pre-built components like Computer Vision and emotion analysis. Whether you’re diving into CNNs, RNNs, or GANs, you'll find yourself seamlessly transforming mundane problems into intelligent solutions. By the end, not only will you understand Azure, you'll be operationalizing models as smoothly as a cat riding a Roomba.
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
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 this book, Amita Kapoor and Sharmistha Chatterjee invite you on a noble quest to turn mysterious, black-box machine learning models into upstanding, transparent citizens. Learn to sniff out and eliminate bias, secure your data pipelines and wrangle the tangled jungle of microservices—all while keeping your models fair, resilient and fully clothed in ethical considerations. By the end, you’ll be constructing models so virtuous they could pass an ethics exam, all while navigating the complex waters of data privacy with the grace of an AI-powered swan.
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
Navin Sabharwal and Amit Agrawal take you on a whimsical journey through Google's magical AI forest, where AutoML fairies and AI Platform gnomes help even the greenest traveler create production-ready AI. With charming examples like predicting loan defaults and categorizing issues, readers will become fluent in Googlish tools like BigQuery, DataPrep and DataProc. Armed with REST APIs and boundless enthusiasm, you'll find that conjuring NLP and machine learning models from thin air isn’t just for Silicon Valley sorcerers anymore.
Download PDF
3. Practical AI on the Google Cloud Platform
2020 by Micheal Lanham
In Micheal Lanham's adventurous how-to manual, you’ll learn that building AI on Google’s Cloud is kind of like assembling a cosmic LEGO set—only these blocks let you create chatbots, analyze images and index video content. Whether you want an AI assistant to order your coffee or help your business sort through mountains of data, this book’s got you covered. With deep dives into Google’s bag of tricks—from Dialogflow to AutoML Tables—you’ll soon be a bona fide AI wizard, casting models and scaling them to tackle feats both great and small.
Download PDF
4. AI as a Service: Serverless machine learning with AWS
2020 by Peter Elger, Eoin Shanaghy
Peter Elger and Eoin Shanaghy present a lively romp through the world of serverless AI with Amazon’s wizard-like cloud infrastructure. Ever wonder how to make a chatbot from virtually nothing? Or have a voice assistant serenade you with data-driven wisdom? This book starts you off with humble projects, letting you slowly transform into a serverless AI conjurer. With an arsenal of serverless templates at your side, you’ll be scaling data pipelines and tackling AI service headaches faster than you can say “cloud magic.”
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
Mathew Salvaris, Danielle Dean and Wee Hyong Tok invite you to Microsoft’s playground of deep learning wonders, Azure. This is your chance to learn how to wield the cloud to conjure up AI marvels—think of it as an enchanted workshop full of pre-built components like Computer Vision and emotion analysis. Whether you’re diving into CNNs, RNNs, or GANs, you'll find yourself seamlessly transforming mundane problems into intelligent solutions. By the end, not only will you understand Azure, you'll be operationalizing models as smoothly as a cat riding a Roomba.
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
