Flexible ML Serving and Management for Retail

Seldon enhances time-to-value so models can get to work up to 85% faster. Democratise machine learning to predict customer behaviour, offer a more personalised experience, and optimise pricing and stock.

Learn why organisations are relying on Seldon, with over 1.7 million models deployed

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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 processes.

of organisations are using machine learning for critical business problems.

faster business growth when powered with AI.

higher average profit margins.

Whatever machine learning means to you, Seldon makes it possible

In order for retail organisations to remain competitive, thrive and deliver sustainable value, volumes of unstructured data to inform customer strategies, supply and pricing has become critical. Whatever maturity you’re on in your journey, Seldon’s flexibility enables your future state.

Predict customer demand to manage stock and inventory
Forecast external influences affecting supply chain performance
Personalise experiences for customers, including product recommendations and tailored advertising
Offer customers alternative, relevant products through substitution models
Optimise pricing and understand elasticity
Analyse customer interaction with virtual assistants and chatbots

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Turn R&D into ROI with Models in Production

Eliminate silos and enable teams to work together in a flexible, secure, single platform that integrates seamlessly with your digital environment and applications.

Self-serve ML through one-click deployments
Platform-agnostic and language agnostic means you can build and deploy ML models your way.
Test new models against historical data, feature distribution and patterns before running in production.
Automate complex deployments leveraging industry-leading techniques such as A/B tests, canaries and multi-arm bandits.

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Optimise your ML Models in Line with Objectives and Compliance

Predicting model outputs and accuracy is key to top and bottom lines as well as brand reputation. Seasons and other external factors can influence model performance, which is why it is important to ensure drift and outliers are identified and managed efficiently.

Proactively mitigate model risk through advanced monitoring techniques to identify drift and outlier detection.
Real-time dashboards and metrics creating transparency across the ML lifecycle.
Model management, monitoring and alerting triggers a faster feedback loop to improve future iterations.
Ensure full auditability, accountability and traceability across all models.

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