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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.

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.

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.

Setting up

Getting Started

Step-by-step guide to get you from zero to forecasting in minutes!

Installation

Guide for installing the Python SDK, essential for forecasting and model tuning.

Advanced topics

Mimosa

Details on Mimosa, our foundation model tailored for time-series forecasting.

Forecasting Guide

Walkthrough of forecasting examples and parameter options.