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Integration Patterns

Common integration patterns and best practices for integrating NeoSpace with your existing systems and workflows.

Integration Architecture​

Overview of integration architecture:

Integration Layers:

  • Data Layer: Connectors for data ingestion
  • Processing Layer: Training and evaluation
  • Serving Layer: Inference servers for predictions
  • Application Layer: Integration with business applications

Integration Points:

  • Data Sources: Connect to your data infrastructure
  • APIs: RESTful APIs for programmatic access
  • Webhooks: Real-time notifications
  • Export: Export results and models

Data Integration Patterns​

Batch Data Integration​

Pattern: Periodic batch data updates.

Use Case: Regular data updates from data warehouses.

Implementation:

  1. Schedule periodic data syncs
  2. Use connectors for batch data access
  3. Create/update datasets automatically
  4. Trigger training on data updates

Best Practices:

  • Schedule during low-traffic periods
  • Validate data before processing
  • Handle incremental updates
  • Monitor sync status

Real-Time Data Integration​

Pattern: Real-time data streaming.

Use Case: Real-time predictions on streaming data.

Implementation:

  1. Connect to streaming data sources
  2. Process data in real-time
  3. Serve predictions via inference servers
  4. Handle high-throughput scenarios

Best Practices:

  • Optimize for low latency
  • Handle backpressure
  • Monitor throughput
  • Scale inference servers

Hybrid Integration​

Pattern: Combination of batch and real-time.

Use Case: Batch training with real-time inference.

Implementation:

  1. Use batch data for training
  2. Use real-time data for inference
  3. Update models periodically
  4. Serve real-time predictions

Best Practices:

  • Separate training and inference pipelines
  • Keep models current
  • Monitor both pipelines
  • Handle versioning

Application Integration Patterns​

API Integration​

Pattern: Integrate via RESTful APIs.

Use Case: Integrate with custom applications.

Implementation:

  1. Use NeoSpace APIs for all operations
  2. Authenticate with API keys
  3. Handle API responses
  4. Implement error handling

Best Practices:

  • Use API keys securely
  • Implement retry logic
  • Handle rate limits
  • Monitor API usage

Webhook Integration​

Pattern: Receive real-time notifications.

Use Case: React to training completion, evaluation results, etc.

Implementation:

  1. Configure webhook endpoints
  2. Subscribe to events
  3. Handle webhook payloads
  4. Process notifications

Best Practices:

  • Verify webhook signatures
  • Handle duplicate events
  • Implement idempotency
  • Monitor webhook delivery

Direct Integration​

Pattern: Direct integration with NeoSpace platform.

Use Case: Embedded workflows in applications.

Implementation:

  1. Embed NeoSpace components
  2. Use platform UI directly
  3. Leverage platform features
  4. Customize as needed

Best Practices:

  • Follow platform guidelines
  • Maintain consistency
  • Ensure security
  • Test thoroughly

Workflow Integration Patterns​

Automated Training Pipeline​

Pattern: Automate model training workflow.

Use Case: Continuous model retraining.

Implementation:

  1. Monitor data changes
  2. Trigger training automatically
  3. Evaluate models automatically
  4. Deploy best models automatically

Best Practices:

  • Validate before automation
  • Monitor automated processes
  • Have manual override
  • Document automation logic

CI/CD Integration​

Pattern: Integrate with CI/CD pipelines.

Use Case: Model deployment in CI/CD workflows.

Implementation:

  1. Train models in CI pipeline
  2. Evaluate in CI pipeline
  3. Deploy in CD pipeline
  4. Monitor in production

Best Practices:

  • Version control models
  • Test before deployment
  • Rollback capability
  • Monitor deployments

Multi-Stage Workflow​

Pattern: Multi-stage workflow with gates.

Use Case: Controlled model promotion.

Implementation:

  1. Train in development
  2. Evaluate in staging
  3. Deploy to production
  4. Monitor at each stage

Best Practices:

  • Define clear gates
  • Validate at each stage
  • Document decisions
  • Track promotions

Best Practices​

Integration Best Practices:

  • Security: Secure all integration points
  • Monitoring: Monitor all integrations
  • Error Handling: Robust error handling
  • Documentation: Document integration patterns
  • Testing: Test integrations thoroughly
  • Versioning: Version integration code
  • Scalability: Design for scalability
  • Maintainability: Keep integrations maintainable

Next Steps​