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

Learn the basics of leveraging Google Cloud Platform for building AI-based applications. Explore the tools that you can use today, as well as best practices for using them correctly.

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