US 12,423,615 B1
Machine learning pipelines
Owen Thomas, Seattle, WA (US); Arun Babu Nagarajan, Redmond, WA (US); Kenneth O Henderson, Everett, WA (US); Weixun Wang, Kirkland, WA (US); and Urvashi Chowdhary, Seattle, WA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Jun. 2, 2021, as Appl. No. 17/337,320.
Claims priority of provisional application 63/118,861, filed on Nov. 27, 2020.
Int. Cl. G06N 20/00 (2019.01); G06F 8/30 (2018.01); G06F 18/21 (2023.01); G06F 18/30 (2023.01); G06F 18/40 (2023.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 8/31 (2013.01); G06F 18/21 (2023.01); G06F 18/30 (2023.01); G06F 18/40 (2023.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
obtaining a programmatic structure of a machine learning (ML) pipeline according to a first programming syntax, the programmatic structure of the ML pipeline comprising data defining a workflow of operations:
obtain data to be used to train an ML model;
train the ML model using the data; and
deploy the ML model to perform inferencing;
processing, according to a second programming syntax, the programmatic structure of the ML pipeline to generate a data interchange format of the ML pipeline, the data interchange format of the ML pipeline comprising a graph representation of the programmatic structure of the ML pipeline, the graph representation comprising at least one node representing at least one of the operations of the workflow, processing the programmatic structure of the ML pipeline including at least using a compiler that converts programing language code of the programmatic structure of the ML pipeline to generate the data interchange format of the ML pipeline; and
causing one or more services to execute at least one of the operations of the workflow based on the data interchange format of the ML pipeline, execution of at least one of the operations triggering obtaining the data to be used to train the ML model, training the ML model using the data, or deploying the ML model to perform the inferencing.