End-to-end AI development platform with DagsHub and OpenShift AI
Accelerate AI from data to deployment with a unified, AI platform that combines dataset curation, experiment tracking, and model versioning in DagsHub with secure, scalable production on Red Hat OpenShift AI.
To develop AI applications and get them from POC to production-grade, teams need to tackle a complex process starting with data curation, annotation, and validation, followed by experimentation (whether it's training a model or prompt engineering), and managing various model versions. Today more than ever, enterprises deal with various data sources to extract the most insights and build the best AI applications for their organizations and customers—images, video, audio, documents, and text—are all used in the AI development lifecylce.
Additionally, AI teams must collaborate effortlessly, sharing versioned datasets and models across functions, while keeping sensitive information on‑premises or within private infrastructure. Balancing multimodal workflows, reproducible collaboration, and self‑hosted security often slows iteration and stifles innovation.
DagsHub removes these barriers—providing a single platform for data versioning, curation, annotation and validation, experiment tracking, and model management. Integrating with OpenShift AI, which provides training and serving for enterprise‑grade orchestration, this combination provides everything enterprises need to deliver high quality AI applications to production. The result is a friction‑free stack that lives entirely on your Kubernetes cluster.
- Unified Traceability & Reproducibility
DagsHub and OpenShift AI create a single source of truth where every dataset version, experiment run, and model artifact is recorded. Dataset versioning via DagsHub Data Engine combined with MLflow’s automatic logging, ensures end-to-end traceability—and OpenShift AI provides the secure infrastructure to host this hub within your private cluster.
- Open Standards Tooling
By leveraging open source formats—Git for code, DVC for data file versioning, MLflow for experiment metadata, and Label Studio for annotations—you can import or export projects at any time. This open stack prevents vendor lock-in and future‑proofs your AI workflows, all underpinned by enterprise‑grade OpenShift AI deployment.
- Enterprise Collaboration Hub
A centralized portal enables teams to explore, search, and share datasets and models with fine‑grained access controls. DagsHub’s web interface organizes artifacts while OpenShift’s role‑based security and monitoring ensure governance and compliance across departments.
- Zero‑Config DevOps Acceleration
Spin up new AI projects with pre‑integrated pipelines: data versioning, labeling, experiment tracking, and model registry come configured out of the box. DagsHub’s GitOps workflow and OpenShift AI’s operator‑driven deployments eliminate custom glue code and drastically reduce DevOps overhead.
- Scalable, Secure Production Delivery
Deploy and serve models at scale with one‑command CI/CD pipelines. GPU‑optimized containers, autoscaling, health checks, and enterprise security features in OpenShift AI ensure your AI services meet production SLAs—all running entirely on your Kubernetes infrastructure.
Combining DagsHub’s data‑centric MLOps with Red Hat OpenShift AI lets teams ship production models 10× faster—without sacrificing traceability.
Dean PlebanCo‑founder & CEO, DagsHub