Sulie is a fully managed platform for time series forecasting, powered by the Mimosa foundation model—a transformer-based model specifically designed to outperform traditional supervised approaches in time series forecasting.

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Key Features

Sulie provides a robust, production-ready solution for data teams, simplifying time series forecasting at scale. Our foundation model offers accurate, out-of-the-box predictions with zero-shot inference, eliminating the need for pre-existing training data. Whether you’re experimenting in a notebook or scaling to production, Sulie abstracts away MLOps complexity, allowing you to focus on actionable insights from your forecasts.

• Zero-Shot Forecasting: Obtain precise forecasts instantly with our foundation model, without requiring training or preprocessing of historical data.
• Auto Fine-Tuning: Enhance model performance with a single API call. We manage the entire training pipeline, providing transparency into model selection and metrics.
• Covariates Support (Enterprise): Conduct multivariate forecasting by incorporating dynamic and static covariates with no feature engineering needed.
• Managed Infrastructure: Focus on forecasting as we manage all aspects of deployment, scaling, and maintenance seamlessly.
• Centralized Datasets: Push time series data continuously through our Python SDK, creating a centralized, versioned repository accessible across your organization.

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Getting Started

1. Generate an API key from our dashboard.
2. Integrate Sulie’s Python SDK into your workflow.
3. Follow our SDK Documentation for a complete guide on implementation.

With Sulie, data teams can unlock value from their forecasts faster and more efficiently, with enterprise-grade capabilities ready to meet complex forecasting needs.

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Setting up

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Advanced topics