Jeyashree Krishnan
Jeyashree Krishnan is a Senior Machine Learning Engineer at Siemens AG. Her work focuses on building and operationalizing scalable machine learning services, with an emphasis on foundation models and time series forecasting. She is also a Visiting Researcher at the Center for Computational Life Sciences, RWTH Aachen University.
Sessions
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.
In an era where new AI models, benchmarks, and frameworks emerge daily, many of us feel caught in a relentless cycle of catching up, what is called "AI fatigue". This talk dives into the causes and consequences of that fatigue, from information overload and social media hype to the constant pressure to stay relevant. Drawing on personal experience and community insights, we explore why chasing every new paper or trend often leads to burnout rather than mastery.
More importantly, we share practical, evidence-backed strategies to stay informed without losing balance: curating a focused “information diet,” setting clear boundaries, using summarization tools intelligently, maintaining a personal knowledge base, and embracing “JOMO”—the joy of missing out. We also discuss how organizations can combat fatigue structurally by promoting focus, curiosity, and psychological safety.
This session is for anyone, from beginners to seasoned professionals, seeking to rediscover genuine curiosity in AI while preserving mental well-being. Attendees will leave with concrete tools, actionable habits, and a renewed sense that it is not only acceptable but healthy to not know everything.