Tag: Google Cloud GCP AI + Machine Learning ML

Google Cloud GCP AI + Machine Learning ML – Create the next generation of applications using artificial intelligence capabilities for any developer and any scenario

Categories
Bibliography Cloud DevOps GCP Software Engineering

Google Cloud Platform in Action – ISBN-13: 978-1617293528

See: Google Cloud Platform in Action, Publisher ‏ : ‎ Manning Publications; 1st edition (August 24, 2018)

Fair Use Source:

Summary

Google Cloud Platform in Action teaches you to build and launch applications that scale, leveraging the many services on GCP to move faster than ever. You’ll learn how to choose exactly the services that best suit your needs, and you’ll be able to build applications that run on Google Cloud Platform and start more quickly, suffer fewer disasters, and require less maintenance.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Thousands of developers worldwide trust Google Cloud Platform, and for good reason. With GCP, you can host your applications on the same infrastructure that powers Search, Maps, and the other Google tools you use daily. You get rock-solid reliability, an incredible array of prebuilt services, and a cost-effective, pay-only-for-what-you-use model. This book gets you started.

About the Book

Google Cloud Platform in Action teaches you how to deploy scalable cloud applications on GCP. Author and Google software engineer JJ Geewax is your guide as you try everything from hosting a simple WordPress web app to commanding cloud-based AI services for computer vision and natural language processing. Along the way, you’ll discover how to maximize cloud-based data storage, roll out serverless applications with Cloud Functions, and manage containers with Kubernetes. Broad, deep, and complete, this authoritative book has everything you need.

What’s inside

  • The many varieties of cloud storage and computing
  • How to make cost-effective choices
  • Hands-on code examples
  • Cloud-based machine learning

About the Reader

Written for intermediate developers. No prior cloud or GCP experience required.

About the Author

JJ Geewax is a software engineer at Google, focusing on Google Cloud Platform and API design.

Table of Contents

  1. What is “cloud”?
  2. Trying it out: deploying WordPress on Google Cloud
  3. The cloud data center
  4. Cloud SQL: managed relational storage
  5. Cloud Datastore: document storage
  6. Cloud Spanner: large-scale SQL
  7. Cloud Bigtable: large-scale structured data
  8. Cloud Storage: object storage
  9. Compute Engine: virtual machines
  10. Kubernetes Engine: managed Kubernetes clusters
  11. App Engine: fully managed applications
  12. Cloud Functions: serverless applications
  13. Cloud DNS: managed DNS hosting
  14. Cloud Vision: image recognition
  15. Cloud Natural Language: text analysis
  16. Cloud Speech: audio-to-text conversion
  17. Cloud Translation: multilanguage machine translation
  18. Cloud Machine Learning Engine: managed machine learning
  19. BigQuery: highly scalable data warehouse
  20. Cloud Dataflow: large-scale data processing
  21. Cloud Pub/Sub: managed event publishing
Categories
Bibliography Cloud GCP

Official Google Cloud Certified Professional Data Engineer Study Guide – B086Q6V6G6

See: Official Google Cloud Certified Professional Data Engineer Study Guide 1st Edition

Fair Use Source:

Categories
Bibliography Cloud GCP

Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform – B08QML28F1

See: Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform 1st ed. Edition

See also Kubernetes and Cloud Native

Fair Use Source:

Categories
Artificial Intelligence Cloud Data Science - Big Data Networking Software Engineering

AIoT – Artificial intelligence of Things

” (WP)

The Artificial Intelligence of Things (AIoT) is the combination of Artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. [1] [2] [3]

See also

References

  1. ^ Ghosh, Iman (12 August 2020). “AIoT: When Artificial Intelligence Meets the Internet of Things”. Visual Capitalist. Retrieved 22 September 2020.
  2. ^ Lin, Yu-Jin; Chuang, Chen-Wei; Yen, Chun-Yueh; Huang, Sheng-Hsin; Huang, Peng-Wei; Chen, Ju-Yi; Lee, Shuenn-Yuh (March 2019). “Artificial Intelligence of Things Wearable System for Cardiac Disease Detection”. 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS): 67–70. doi:10.1109/AICAS.2019.8771630. Retrieved 22 September 2020.
  3. ^ Chu, William Cheng-Chung; Shih, Chihhsiong; Chou, Wen-Yi; Ahamed, Sheikh Iqbal; Hsiung, Pao-Ann (November 2019). “Artificial Intelligence of Things in Sports Science: Weight Training as an Example”. Computer. 52 (11): 52–61. doi:10.1109/MC.2019.2933772. ISSN 1558-0814. Retrieved 22 September 2020.

Categories: 

” (WP)

Sources:

Fair Use Sources: