2021.beamsummit.org/sessions/lessons-learned-dataflow-ml-batch-inference

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https://2021.beamsummit.org/sessions/lessons-learned-dataflow-ml-batch-inference

Lessons learned from using Dataflow for local ML batch inference

The Beam Summit brings together experts and community to share the exciting ways they are using, changing, and advancing Apache Beam and the world of data and stream processing.



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Lessons learned from using Dataflow for local ML batch inference

https://2021.beamsummit.org/sessions/lessons-learned-dataflow-ml-batch-inference

The Beam Summit brings together experts and community to share the exciting ways they are using, changing, and advancing Apache Beam and the world of data and stream processing.



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https://2021.beamsummit.org/sessions/lessons-learned-dataflow-ml-batch-inference

Lessons learned from using Dataflow for local ML batch inference

The Beam Summit brings together experts and community to share the exciting ways they are using, changing, and advancing Apache Beam and the world of data and stream processing.

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      Lessons learned from using Dataflow for local ML batch inference
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      At BenchSci, we mine the world’s biological research papers with the aim of extracting information that will accelerate future pharmaceutical research programs by enabling more reproducible experiments. Machine Learning and specifically our in-house Deep Learning models play an important role in extracting these key pieces of information and organizing the knowledge into a meaningful and easy-to-use structure. While we have the luxury of processing this information in batch, the size and number of our models along with the size of the input data eventually outgrew our on-premise model serving infrastructure.
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      Lessons learned from using Dataflow for local ML batch inference
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      At BenchSci, we mine the world’s biological research papers with the aim of extracting information that will accelerate future pharmaceutical research programs by enabling more reproducible experiments. Machine Learning and specifically our in-house Deep Learning models play an important role in extracting these key pieces of information and organizing the knowledge into a meaningful and easy-to-use structure. While we have the luxury of processing this information in batch, the size and number of our models along with the size of the input data eventually outgrew our on-premise model serving infrastructure.

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