In Part I, we introduced you to a plethora of AI offerings from AWS and grouped them into two categories:
- AI services
- ML platforms
Our recommendation to you is to first leverage AWS-managed AI services, such as Rekogntion, Translate, and Comprehend, in your solution development. Only when there is a need for custom AI capabilities should you then build them with AWS ML platforms such as SageMaker. This approach will improve your speed to market and the return on investment for your intelligent-enabled applications. We also explained that the true power of developing intelligent-enabled solutions on AWS is to combine AWS AI offerings with the rest of the AWS cloud computing ecosystem, including S3, DynamoDB, and EMR.
We also discussed architecture design for AI applications and how a well-designed architecture allows for rapid iteration...
