sktime is a widely used scikit-learn compatible library for learning with time series. sktime is easily extensible by anyone, and interoperable with the pydata/numfocus stack.

This talk presents progress, challenges, and newest features off the press, in extending the sktime framework to deep learning and foundation models.

Recent progress in generative AI and deep learning is leading to an ever-exploding number of popular “next generation AI” models for time series tasks like forecasting, classification, segmentation.

Particular challenges of the new AI ecosystem are inconsistent formal interfaces, different deep learning backends, vendor specific APIs and architectures which do not match sklearn-like patterns well – every practitioner who has tried to use at least two such models at the same time (outside sktime) will have their individual painful memories.

We show how sktime brings its unified interface architecture for time series modelling to the brave new AI frontier, using novel design patterns building on ideas from hugging face and scikit-learn, to provide modular, extensible building blocks with a simple specification language.