Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Deployment of AI Time-series Models for IoT   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.

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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
2
The categories of this paper are: math
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin