Best Practices for Analytics and Generative AI on AWS

Overview

Discover best practices for optimizing data pipelines, analytics, and generative AI applications on AWS while maximizing performance and cost efficiency in cloud-based data architectures.

Syllabus

• Introduction to AWS for Data Analytics and AI
Overview of AWS services for analytics and AI
Understanding cloud-based data architectures
• Data Pipelines on AWS
Designing scalable and efficient data pipelines
Key AWS services: AWS Glue, Amazon Kinesis, and Amazon Redshift
Best practices for ETL processes and data lakes
• Optimizing Analytics on AWS
Performance tuning for Amazon Redshift and other databases
Cost management and optimization strategies
Leveraging Amazon Athena for interactive queries
• Generative AI on AWS
Introduction to generative AI and its applications
Utilizing Amazon SageMaker for model training and deployment
Integrating AWS AI services: AWS DeepComposer, AWS DeepRacer
• Security and Compliance
Implementing security best practices in AWS analytics and AI environments
Compliance frameworks and AWS tools for data protection
• Performance and Cost Efficiency
Monitoring and optimizing performance with AWS CloudWatch
Strategies for cost reduction in analytics and AI workloads
• Case Studies and Real-world Applications
Successful implementations of analytics and generative AI on AWS
Lessons learned and insights from industry experts
• Course Conclusion and Future Trends
Emerging trends in AI and analytics on the cloud
Future opportunities with AWS enhancements in AI and analytics

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Subjects

Data Science