AI Playground Limitations
AWS AI/ML playground restrictions and limitations for safe learning
Why These Limitations?
These limitations are designed to ensure a safe, controlled learning environment while preventing accidental costs and maintaining platform stability. They allow you to learn real cloud concepts without the risks associated with production environments.
Cost Protection
Security
Performance
AI/ML Service Limitations
AI/ML Service Limits
- SageMaker notebooks limited to ml.t3.medium instances
- No GPU-powered SageMaker instances
- Maximum 2 SageMaker notebooks per playground
- No SageMaker training job creation
- No SageMaker endpoint deployment
- No Bedrock model access or fine-tuning
Compute Restrictions
- No EC2 GPU instances (g4dn, p3, p4, etc.)
- No Spot instance usage
- No dedicated hosts or reserved instances
- Maximum 4 vCPUs per playground
- Maximum 16GB RAM per playground
- No batch processing or HPC clusters
Data Limitations
- Maximum 50GB total data storage
- No S3 bucket creation for training data
- No EFS file system creation
- No FSx for Lustre or Windows File Server
- Limited to demo datasets provided
- No external data source connections
Model Restrictions
- No custom model training on large datasets
- No model deployment to production endpoints
- No model versioning or A/B testing
- No custom algorithm development
- No hyperparameter optimization jobs
- Limited to pre-trained model exploration
Integration Limits
- No external API integrations
- No real-time data streaming
- No IoT device connections
- No mobile app integration
- No webhook configurations
- No third-party service connections
General Limitations
Session Duration
All playground sessions are limited to 1 hour with up to 2 extensions (3 hours total)
- •Automatic termination after session expiry
- •No manual session extension beyond limits
- •Data preservation for 24 hours after session end
Cost Protection
All playgrounds are isolated and cannot incur real AWS charges
- •No billing access or cost management
- •All resources are sandboxed
- •No real AWS account charges possible
Data Privacy
Your playground data is isolated and secure
- •No data sharing between users
- •Automatic data cleanup after session
- •No persistent data storage beyond session
What You Can Do
Learning Activities
- • Explore AWS services and features
- • Practice configuration and management
- • Test different instance types and configurations
- • Learn networking concepts and security
- • Experiment with storage options
- • Explore SageMaker notebooks and features
- • Practice data preprocessing and analysis
- • Learn ML model evaluation techniques
Best Practices
- • Plan your session before starting
- • Use demo resources efficiently
- • Document your configurations
- • Practice security best practices
- • Learn cost optimization techniques
- • Understand service limitations
Recommendations
For Learning
- • Start with basic services and gradually explore advanced features
- • Use the demo resources provided to understand service capabilities
- • Practice with different configurations to understand trade-offs
- • Focus on understanding concepts rather than building production systems
- • Use the session time efficiently by planning your learning objectives
For Production
- • Use real AWS accounts for production workloads
- • Implement proper security and access controls
- • Set up billing alerts and cost monitoring
- • Use AWS Organizations for multi-account management
- • Follow AWS Well-Architected Framework principles
Ready to Start Learning?
Understanding these limitations helps you make the most of your learning experience.