external
Data Sources
Databases, APIs, event streams, and file uploads feeding raw training data.
End-to-end ML platform with data ingestion, feature engineering, training, serving, and monitoring.
Databases, APIs, event streams, and file uploads feeding raw training data.
Apache Spark/Beam pipelines for ETL, deduplication, and schema validation.
Centralized feature repository with versioning, serving, and offline/online sync.
Distributed training on GPU clusters with hyperparameter tuning and experiment tracking.
Versioned model artifacts with metadata, lineage, and A/B test assignments.
Low-latency inference API with batching, caching, and canary deployments.
Data drift detection, prediction quality tracking, and automated retraining triggers.
Jupyter-based experimentation environment with GPU access and shared datasets.
Explore this architecture with animated data flows, node auditing, and AI-powered analysis.
Open in Codelit Use Plan & ShipA single application with private file storage, tenant-scoped records, and idempotent billing events. Start small; scale from measured demand.
5 components · 4 connectionsPreserve original evidence separately from model suggestions and require review before publishing roadmap changes.
5 components · 4 connectionsKeep the approved result, the rendered write, and the provider receipt separate so a retry cannot silently duplicate an external action.
5 components · 5 connectionsModern SaaS with microservices, event-driven processing, and multi-tenant architecture.
10 components · 9 connections