English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 19m | 127 MB
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
Table of Contents
1 Intro to artificial intelligence (AI) on Google
2 What you should know
3 AI processing and Google
4 Create a knowledge base
5 AI applications and Google
6 AI and cloud computing
7 AI and Google
8 Case study International Drone Inc
9 Identifying the need for AI
10 AI solution Better inventory control
11 AI solution Better manufacturing systems
12 ROI of AI inclusion
13 Vision AI build
14 Vision AI training
15 Vision AI deployment
16 Demo Vision AI
17 Kubeflow overview
18 Set up Kubeflow
19 Kubeflow integration
20 Execution
21 Identify requirements
22 Design an AI system for GCP
23 Build
24 Train
25 Deployment
26 AI’s impact on performance
27 Estimate cost of AI integration
28 Operations best practices
29 Security considerations
30 Governance
31 Additional resources
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