Connect model development, verification, and validation with automated evidence collection.
Capture results through the SDK or APIs and apply automated checks to submitted evidence. Reviewers assess the results, rationale, and limitations against requirements, preserving decisions for team and customer handovers.
Compare training and evaluation results in shared dashboards. Keep each model candidate’s runs, settings, and files together so teammates can understand what worked and why.
Find shared devices and reserve time with Device Farm. Compare accuracy, latency, and memory usage alongside the device and test settings behind each measurement.
Keep your existing tools and connect their results through the SDK or APIs. Run the Hub in your own environment: on-premises, in your private cloud, or air-gapped.
Results often lose their context when work moves between development, validation, and customer teams. The Hub keeps experiments, models, hardware results, and requirements connected, so teams can understand decisions and build on each other’s work.
ML and embedded engineers, safety and validation teams, and engineering and product leaders developing physical AI. Platform teams can deploy the Hub in their own environment and connect it to existing tools and processes.
Connected runs, models, files, and results preserve the context behind a model choice. Evidence submissions link results to requirements, engineer rationale, and review decisions, with revision history showing how the evidence developed.
Evidence Collection links requirements to recorded results, automated checks, and human reviews. It supports your assurance process; passing a check or accepting evidence does not itself establish product compliance.
No. Your pipelines keep running where they are. Use the Python SDK or HTTP APIs to record results, and link requirements to their authoritative records in other systems.
Yes. Deployment packages support Docker Compose and Kubernetes, including air-gapped setups. Your administrator manages installation and upgrades. Embedl’s hosted environment lets you evaluate the product before planning your own deployment.
Yes. Configure your device inventory or connect an Embedl Device Farm on your network. Reserve devices, run evaluations with your own tools, and record the results and test conditions through the SDK.
Projects organize work and access. Engineers share runs, dashboards, models, and files; reviewers assess evidence and return feedback. Teams can inspect results in the Embedl Hub web app without needing access to the environment that produced them.