Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
1h 20mIntermediate2020-02-20
Authors
David Linthicum
Chief Cloud Strategy Officer at Deloitte Consulting
Course details
In order to successfully leverage AI on Google Cloud Platform (GCP), you must understand what AI is and become familiar with the native tools that GCP offers. This practical course takes you through the basics of leveraging GCP for AI-based applications, including the tools that you can leverage today and how to use them correctly. Instructor David Linthicum introduces Vision AI, a key image identification product from Google, as well as Kubeflow, the machine learning (ML) toolkit designed to simplify the process of deploying ML workflows on Kubernetes. Throughout the course, David presents a variety of real-world use cases that illustrate how these concepts work in practice.
Topics include:
- Creating a knowledge base
- AI and cloud computing
- ROI of the inclusion of AI within a business system
- Working with the Vision AI tool
- The basics of using Kubeflow
- Designing AI systems for GCP AI services
- AI-based security in GCP
- Estimating the cost of AI integration
Topics include:
- Creating a knowledge base
- AI and cloud computing
- ROI of the inclusion of AI within a business system
- Working with the Vision AI tool
- The basics of using Kubeflow
- Designing AI systems for GCP AI services
- AI-based security in GCP
- Estimating the cost of AI integration
Skills covered
Google CloudMachine LearningSoftware Development ToolsGoogleCloud PlatformsArtificial Intelligence (AI)Cloud ComputingSoftware DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Intro to artificial intelligence (AI) on Google
- 02 - What you should know
1. AI Basics
- 03 - AI processing and Google
- 04 - Create a knowledge base
- 05 - AI applications and Google
- 06 - AI and cloud computing
- 07 - AI and Google
2. Sample AI Use Case
- 08 - Case study - International Drone Inc.
- 09 - Identifying the need for AI
- 10 - AI solution - Better inventory control
- 11 - AI solution - Better manufacturing systems
- 12 - ROI of AI inclusion
3. GCP Vision AI
- 13 - Vision AI build
- 14 - Vision AI training
- 15 - Vision AI deployment
- 16 - Demo - Vision AI
4. GCP Kubeflow
- 17 - Kubeflow overview
- 18 - Set up Kubeflow
- 19 - Kubeflow integration
- 20 - Execution
5. GCP AI Application Walk-Through
- 21 - Identify requirements
- 22 - Design an AI system for GCP
- 23 - Build
- 24 - Train
- 25 - Deployment
6. Other Considerations
- 26 - AI's impact on performance
- 27 - Estimate cost of AI integration
- 28 - Operations best practices
- 29 - Security considerations
- 30 - Governance
Conclusion
- 31 - Additional resources