Reza Rokni

This session will provide an overview of how to utilize large language models (LLMs) using Apache Beam’s RunInference framework. The prime example for the talk will be running the Gemma open model on Dataflow, outlining considerations and common pitfalls when writing pipelines with LLMs.

In this talk, we will make use of Apache Beam and the Apache Flink runner along with the RunInference transform from the tfx-dsl library to build several inference pipelines, from single models to multiple models in chained or branched configurations.

In this talk, we will make use of the RunInferene transform from the tfx-dsl library to build several inference pipelines, from single models to multiple models in chained or branched configurations.

In this session we will walk through using Apache Beam and Tensor Flow Extended (TFX) to build a Beam pipeline that is continuously outputting timeseries data which is used by TFX for re-training of a model.

Learning a whole new SDK and style to write massively parallel data pipelines is certainly a daunting task for any engineer, data scientist, or developer. We have tried to build an end-to-end example of an Apache Beam pipeline that utilizes various valuable Beam features, and tries to establish...

We will implement a Google Cloud Dataflow pipeline through a series of labs where we will explore functionalities like auto-scaling, monitoring and optimization.