Show HN: Python SDK – forecasting with foundation time-series and tabular models (github.com)

A new Python SDK, FAIM (Foundation AI Models), has been launched to streamline forecasting with both time-series and tabular models. This production-ready SDK incorporates advanced features such as multiple foundation models like FlowState, Amazon Chronos 2.0, and TiRex for time-series predictions, alongside LimiX for tabular regression and classification tasks. Its type-safe API, built with Pydantic validation, ensures robust error handling, while optimized Apache Arrow serialization significantly enhances performance through zero-copy operations. The SDK also supports asynchronous requests, enabling efficient processing of multiple forecasts concurrently. The FAIM SDK is significant for the AI/ML community as it simplifies the integration of complex forecasting models into applications, catering to both practitioners and researchers in predictive analytics. By providing probabilistic outputs, including quantiles and samples, it facilitates comprehensive uncertainty estimation in forecasts. Additionally, the ability to process both time-series and tabular data under a unified framework positions FAIM as a versatile tool, encouraging broader adoption of foundation models across various data types. Overall, this SDK aims to enhance the accessibility and functionality of AI-driven forecasting solutions.