Accelerate Analytics and AI Innovation with a Unified Data Foundation on AWS

Overview

Discover how AWS empowers customers with innovative storage, analytics, and AI solutions, focusing on Amazon S3 and SageMaker to create a unified data foundation with minimal operational burden.

Syllabus

• Introduction to AWS for Analytics and AI
Overview of AWS services
Importance of a unified data foundation
Course objectives and structure
• Understanding Amazon S3 for Data Storage
Key features and benefits of Amazon S3
S3 storage classes and data lifecycle management
Best practices for data storage in S3
• Data Analytics on AWS
Introduction to AWS analytics services
Using AWS Glue for ETL processes
Exploring Amazon Athena for serverless querying
• Amazon SageMaker for AI and Machine Learning
Overview of Amazon SageMaker capabilities
Building, training, and deploying ML models
Integration of SageMaker with other AWS services
• Creating a Unified Data Foundation
Strategies for data integration across AWS services
Data governance and security considerations
Automating workflows with AWS Lambda and Step Functions
• Case Studies and Real-world Applications
Success stories using AWS storage, analytics, and AI solutions
Industry-specific scenarios and best practices
• Hands-on Labs and Practical Exercises
Setting up and configuring an Amazon S3 bucket
Conducting data analysis with Amazon Athena
Developing a ML model using SageMaker
• Monitoring and Optimizing Performance
Implementing AWS CloudWatch for monitoring
Cost optimization strategies for storage and compute
• Conclusion and Next Steps
Review of key concepts and tools
Additional resources and learning paths
Q&A session and feedback collection

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Subjects

Programming