Webinar 6: Distributed Processing for Machine Learning Production Pipelines

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Register via this link: https://learn.xnextcon.com/event/eventdetails/W20061010

Talk: Distributed Processing for Machine Learning Production Pipelines by Reza Rokni (Developer advocate, Cloud Dataflow) and Robert Crowe (Developer Advocate, Tensorflow)

Abstract:
Production ML workloads often require very large compute and system resources, which leads to the application of distributed processing on clusters. On premises or cloud-based infrastructure cost requires maximum efficient use of resources. This makes distributed processing pipeline frameworks such as Apache Beam ideal for ML workloads.
In addition, production ML must address issues of modern software methodology, as well as issues unique to ML. Different types of ML have different requirements, often driven by the different data lifecycles and sources of ground truth. Implementations often suffer from limitations in modularity, scalability, and extensibility.

In this talk, we discuss production ML applications and review TensorFlow Extended (TFX), Flink, Apache Beam, and Google experience with ML in production.

Start date/time: June 25, 9AM PST/ 6PM CEST

RSVP: https://learn.xnextcon.com/event/eventdetails/W20061010

This is the the 6th and last session in Beam Learning Month series. You can access all previous recordings and slides here: https://github.com/aijamalnk/beam-learning-month

If you liked this series, please let us know if you want to have more of them in future by filling out this survey: https://docs.google.com/forms/d/1uw3gvhp7gHmrFiPixYoQcpBqnUbjJhABf0KCGsFekn4/edit#responses

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