Production Machine Learning Management at Scale

Seldon enhances time-to-value so models can get to work up to 85% faster. Scale with confidence and minimise risk through interpretable results and transparent model performance.

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With Seldon Deploy, your business can efficiently serve, monitor and explain ML models, resulting in optimised performance, and greater understanding of how your models impact decisions and business processes.

of organisations are using machine learning for critical business problems.

faster business growth when powered with AI.

higher average profit margins.

Turn R&D into ROI with Models Served in Production

Serve, manage and manipulate models and workflows securely through an intuitive user interface, hosted either on premise or in the cloud.

Front end deployment of models, explainers and canaries offer huge time savings throughout the development process and enhanced collaboration across the business.
Teams have complete control over model deployment and management workflows, allowing hurdles to be removed.
Platform-agnostic and language agnostic means you can build and deploy ML models your way.
Test new models against historical data before running in production.

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Monitor and Optimise ML Models

Continuous monitoring of data with model metrics detects when data drifts or changes unexpectedly.

Dashboards and metrics offer more confidence in the performance of models through enhanced visibility and understanding of MLOps.
Model management, monitoring and alerting tools give developers a faster feedback loop, so they can improve future iterations more quickly.
Deployment strategies such as canary and AB testing mean models are constantly optimised and you can be confident the best performing model is running.

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Explain Models and Ensure Compliance

Seldon allows stakeholders to understand why a model is performing the way it is. Having the right information at the right time allows you to answer to internal auditors and external regulators more quickly, and avoid non-compliance fines through successful model governance.

Understand model behavior and influences, and audit your model when it begins to become less effective.
Holistic monitoring allows insight across the organisation.
Ensure full accountability and traceability across all models.
Integrated compliance capabilities help you to keep models within risk requirements.

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