Catarina Filipe
Hi, I'm Catarina! I've been a Data Scientist at Siemens for the past 4 years. I'm now focused on the mission of unifying all time series related topics under one roof. I'm also happy to be back in Darmstadt, the city where I completed part of my Master's degree, to talk about Python.
Session
Time series foundation models (TSFMs) such as Chronos, Lag-Llama, TimesFM, and Siemens’ own GTT have shown strong generalization capabilities across diverse forecasting tasks. However, integrating these models into a large organization is primarily a software engineering and MLOps challenge rather than a modeling one.
In this talk, we present a real-world case study based on Siemens KPI Forecast, a Python-based forecasting platform that operationalizes multiple TSFMs as reusable, production-grade services. The platform integrates both open research models and Siemens-developed models behind a unified API, supporting zero-shot inference, fine-tuning jobs, and fine-tuned inference depending on user needs and operational constraints.
We focus on how Python is used to compose heterogeneous components including open and closed-source models, internal data products, APIs, and orchestration layers into a consistent time series specialist user experience. The session also covers challenges operating such services within a B2B environment, including issues related to monitoring, versioning, and governance.
Attendees will gain practical insights into turning TSFMs into reliable Python services that scale across teams and use cases.