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Build AI Infrastructure with Google Cloud Platform (GCP)

Build AI Infrastructure with Google Cloud Platform (GCP)

1h 7mIntermediate2024-11-18

Authors

Zarina Meeran

Zarina Meeran

Senior Cloud System Developer at Cloudreach | Azure, GCP, CCNA

Course details

In this course, Zarina Meeran—a Senior Cloud System Developer—gets you started on a journey to master Google Cloud Platform AI tools and infrastructure. This course is suited for developers, IT professionals, and anyone enthusiastic about leveraging cloud computing to enhance AI and ML development productivity. Begin with an introduction to GCP AI Infrastructure and the significance of managing infrastructure for AI applications. Explore the usage and benefits of Vertex AI and how it streamlines the machine learning (ML) lifecycle. Focus on notebooks, and learn how TensorFlow integrates into AI development. Grasp the computational requirements of machine learning algorithms, including when and how to upgrade to GPU or TPU for processing. Dive into image classification, deploying models, and setting up user-managed notebooks in Vertex AI Workbench. Whether you aim to innovate or optimize, this course equips you with the knowledge to confidently implement AI solutions in the cloud.

Skills covered

Google Cloud PlatformArtificial Intelligence FoundationsSoftware Development ToolsGoogleFoundationsCloud PlatformsArtificial Intelligence (AI)Cloud ComputingSoftware Development

Concepts

0. Introduction

  • 01 - Implement AI solutions in the cloud

1. Google Cloud Platform (GCP) AI Infrastructure

  • 02 - Importance of managing infrastructure for AI
  • 03 - Traditional vs. automating ML using GCP

2. Vertex AI

  • 04 - What is Vertex AI
  • 05 - MLOps with Vertex AI
  • 06 - Vertex AI walkthrough
  • 07 - Vertex AI use cases

3. GCP AI Infrastructure

  • 08 - Notebooks
  • 09 - Running AI in GCP Notebooks
  • 10 - TensorFlow
  • 11 - TensorFlow walkthrough
  • 12 - CPU, TPU, and GPU

4. Vertex AI Workbench

  • 13 - Image classification tutorial
  • 14 - Deploying and evaluating your model
  • 15 - Setting up a user managed notebook

Conclusion

  • 16 - Next steps

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