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Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications

Via LinkedIn Learning

LinkedIn Learning based on 72 ratings

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Overview

This course focuses on applying machine learning solutions on Google Cloud Platform for real-world challenges. Learners work with tools like Big Query ML, Auto ML, and Vertex AI to build scalable predictive models. The curriculum includes case studies in finance, healthcare, and retail. Data scientists and engineers gain practical experience deploying cloud-native ML workflows.

Syllabus

Introduction
  • Intro to artificial intelligence (AI) on Google
  • What you should know
1. AI Basics
  • AI processing and Google
  • Create a knowledge base
  • AI applications and Google
  • AI and cloud computing
  • AI and Google
2. Sample AI Use Case
  • Case study: International Drone Inc.
  • Identifying the need for AI
  • AI solution: Better inventory control
  • AI solution: Better manufacturing systems
  • ROI of AI inclusion
3. GCP Vision AI
  • Vision AI build
  • Vision AI training
  • Vision AI deployment
  • Demo: Vision AI
4. GCP Kubeflow
  • Kubeflow overview
  • Set up Kubeflow
  • Kubeflow integration
  • Execution
5. GCP AI Application Walk-Through
  • Identify requirements
  • Design an AI system for GCP
  • Build
  • Train
  • Deployment
6. Other Considerations
  • AI's impact on performance
  • Estimate cost of AI integration
  • Operations best practices
  • Security considerations
  • Governance
Conclusion
  • Additional resources
Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
Go to Class

via LinkedIn Learning

1 hour 20 minutes

Certificate Available

English

On-Demand

Instructor

David Linthicum

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