PipeLime

Pipelime is a data-centric AI platform that turns raw, unstructured files into production-ready datasets without writing code. Built for computer-vision teams, it automates every stage of the data journey—from ingestion to model training—so users can ship robust models in days instead of months.

Main introduction
The homepage positions Pipelime as “the fastest way to clean, label and augment data for computer vision.” Instead of stitching together point tools, teams upload images or videos to a single cloud workspace where AI agents handle curation, quality assurance, labeling, synthetic augmentation, and export to any training framework. No-code workflows let non-engineers iterate, while Python SDKs and REST APIs give ML engineers full programmatic control. The platform is already used by enterprises to cut data-preparation costs by 70 % and reduce time-to-model by 10×.

Key features
1. AI-powered data cleaning – automatic blur, duplicate, and NSFW detection with one-click removal.
2. Agentic labeling – active-learning models pre-label objects, classification, and segmentation; human reviewers only correct edge cases, shrinking annotation effort by 80 %.
3. Synthetic augmentation – on-the-fly generation of lighting, weather, and camera-variation images to boost rare classes and balance datasets without collecting new data.
4. Collaborative workspace – role-based access, real-time commenting, and granular audit trails align annotators, domain experts, and ML teams.
5. One-click export – native integrations with YOLO, COCO, Pascal VOC, TensorFlow, PyTorch, and cloud buckets; versioned datasets can be rolled back instantly.
6. Security & compliance – SOC 2 Type II, GDPR, and HIPAA ready; data stays encrypted at rest and in transit with optional on-prem deployment.
7. SDK & automation – Python CLI and REST endpoints let users schedule nightly jobs that sync new footage, retrain labeling agents, and push updated datasets to CI/CD pipelines.