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 CodelitModern SaaS with microservices, event-driven processing, and multi-tenant architecture.
10 components · 9 connectionsProduction checkout flow with Stripe payments, inventory management, and fraud detection.
11 components · 11 connectionsContinuous integration and deployment system with parallel jobs, artifact caching, and environment management.
9 components · 9 connectionsWorkspace-based team messaging with channels, threads, file sharing, and integrations.
9 components · 9 connections