MLOps meets XAI

Deploy models with confidence

Responsible & explainable MLOps

  • A safe and responsible MLOps environment
  • Explain and understand AI decisions
  • Traceback how decisions are made

Manage, Deploy, and Update

Easily deploy and update AI models

  • Deploy and update your AI models through simple steps
  • Collaborate using Deeploy’s UI, API, and Python SDK
  • Use API endpoints to integrate Deeploy into your application

Explainable AI

Explain model decisions to everyone

  • Support deployments with local and global explainers
  • Add custom explainers tailored to your end-users
  • Traceback explanations of historical predictions

Find out more about how we define explainable AI

Feedback loop

Improve model performance with human feedback

  • Facilitate interactions between AI and humans
  • Enable domain experts to evaluate predictions
  • Receive real-time feedback on models and predictions

Read how DDFinance uses feedback loops to improve their AI models

Integrations

Seamlessly integrates into your current workflow

  • Run Deeploy on your current cloud platform (AWS, GCP, Microsoft Azure)
  • Incorporate into existing code workflows (GitLab, CircleCI)
  • Receive notifications through Slack, mail and other channels

Audit trails

Reproduce and traceback decisions & explanations

  • Support for processes to comply with AI regulation
  • Trace and reproduce every change, prediction, explanation, and deployment
  • Ensure clear ownership of Deployments and Workspaces

Read how Coeo uses audit trails within their claim handling process

Monitoring and alerting

Effectivily monitor models, predictions and interactions

  • Constantly monitor model drift, bias detection, and performance
  • Define custom alerts for the metrics that matter to you
  • Easily discover the root cause of model degradation