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Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

1h 28mIntermediate2019-10-22

Authors

David Linthicum

David Linthicum

Chief Cloud Strategy Officer at Deloitte Consulting

Course details

The cost and efficiency of the cloud puts machine learning and artificial intelligence (AI) within the grasp of enterprises big and small. Help your organization tap into their power with Amazon Web Services. This course is a practical approach to leveraging AWS for AI-based applications across a variety of industries, including healthcare, finance, law enforcement, manufacturing, and education. Instructor David Linthicum introduces SageMaker, Amazon’s AI platform, and presents a variety of use cases that demonstrate current best practices, tools, and techniques. He shows how to build and train machine learning models with SageMaker, and integrate them into real-world apps. David also dispels some concerns around AI, such as cost and security, by showcasing real AWS solutions.

Learning objectives
AI basics
AI use cases
Building, training, and deploying apps with SageMaker
Creating test data and training your SageMaker model
AI application walk-through
AI costs
AI security
AI governance

Skills covered

Cloud DevelopmentMachine LearningAmazon Web Services (AWS)AmazonCloud ServicesArtificial Intelligence (AI)Cloud ComputingOne-Off

Concepts

0. Introduction

  • 01 - Tap into the power of artificial intelligence (AI) with AWS
  • 02 - AI on AWS
  • 03 - What you should know

1. AI Basics

  • 04 - AI processing
  • 05 - Knowledge creation
  • 06 - AI applications
  • 07 - AI and cloud computing
  • 08 - AI and AWS

2. AI Use Cases

  • 09 - Healthcare
  • 10 - Finance
  • 11 - Law enforcement
  • 12 - Manufacturing
  • 13 - Education

3. AWS SageMaker

  • 14 - SageMaker build
  • 15 - SageMaker train
  • 16 - SageMaker deploy
  • 17 - Create a SageMaker notebook
  • 18 - Create test data and train the model

4. AWS SageMaker Ground Truth

  • 19 - What's different
  • 20 - Use case

5. AI Application Walkthrough

  • 21 - Requirement
  • 22 - Design
  • 23 - Build
  • 24 - Train
  • 25 - Deploy

6. Other Considerations

  • 26 - Performance
  • 27 - Cost
  • 28 - Operations
  • 29 - Security
  • 30 - Governance

Conclusion

  • 31 - Next steps

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