# Scalable Deployment of AI Time-series Models for IoT

arXiv: https://arxiv.org/abs/2003.12141 · HTML: https://mlsanity.com/paper/2003.12141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [A monitoring framework for deployed machine learning models with supply chain examples](https://mlsanity.com/paper/2211.06239/) (2022)
- [Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks](https://mlsanity.com/paper/2103.07248/) (2021)

## Abstract (arXiv)

IBM Research Castor, a cloud-native system for managing and deploying large numbers of AI time-series models in IoT applications, is described. Modelling code templates, in Python and R, following a typical machine-learning workflow are supported. A knowledge-based approach to managing model and time-series data allows the use of general semantic concepts for expressing feature engineering tasks. Model templates can be programmatically deployed against specific instances of semantic concepts, thus supporting model reuse and automated replication as the IoT application grows. Deployed models are automatically executed in parallel leveraging a serverless cloud computing framework. The complete history of trained model versions and rolling-horizon predictions is persisted, thus enabling full model lineage and traceability. Results from deployments in real-world smart-grid live forecasting applications are reported. Scalability of executing up to tens of thousands of AI modelling tasks is also evaluated.