The landscape of time-series forecasting is undergoing a seismic shift. With the emergence of foundation models like Chronos 2 and TimesFM, the industry is at a crossroads: can a large-scale pre-trained model truly replace the specialized, "local" models that practitioners have spent years tuning?

In this talk, we move beyond theoretical benchmarks to provide a transparent look at testing time-series foundation models in production-like environments. We explore the transition from traditional statistical and machine learning methods to generative architectures, focusing on the practical challenges that arise when "zero-shot" capabilities meet the messy reality of business data.

What you will learn:

• The Foundation Model Landscape: A high-level mapping of the current state-of-the-art and how these architectures differ from classical statistical and ML approaches.
• Zero-Shot vs. Reality: How pre-trained models handle domain-specific context and exogenous business drivers—such as promotions, seasonality, and market shocks—without explicit training.
• The Operational Shift: How moving toward foundation models changes the MLOps lifecycle,from data preparation to running inference at scale
• Predictive Stability & Trust: A framework for evaluating whether a model is "production-ready," focusing on forecast stability and consistency of predictions over time.
• A Decision Roadmap: A practical checklist for teams looking to integrate these models into their stack without sacrificing reliability.

Whether you are a data scientist looking to upgrade your forecasting pipeline or a lead evaluating the impact of Foundation Models on time-series workflows, this session offers a grounded, hype-free perspective from the front lines of implementation.