Great work! Thanks a lot for your contributions to Beam!!!

On Wed, Sep 24, 2025 at 11:36 AM Charles Nguyen <[email protected]> wrote:

> Hi Beam community,
>
> With my GSoC 2025 project concluded, I recently wrote up a blog
> https://beam.apache.org/blog/gsoc-25-yaml-user-accessibility/ about my
> experience working on the project.
>
> The work includes example pipelines and workflows for ML use cases with
> Kafka and Iceberg data sources, using the YAML SDK:
>
>    - *Streaming Classification Inference
>    
> <https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/sentiment_analysis>:*
>  A
>    pipeline that performs a sentiment analysis task on a stream of YouTube
>    comments read from Kafka. The overall workflow also includes DistilBERT
>    model deployment and serving on Google Cloud Vertex AI where the pipeline
>    can access for remote inferences.
>
>    - *Streaming Regression Inference
>    
> <https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/taxi_fare>*:
>    A pipeline that performs taxi fare amount predictions on a stream of taxi
>    rides read from Kafka. The overall workflow also includes custom model
>    deployment and serving on Google Cloud Vertex AI where the pipeline can
>    access for remote inferences.
>
>    - *Batch Anomaly Detection
>    
> <https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/log_analysis>*:
>    A workflow containing model training and several pipelines that leverage
>    Iceberg for storing results, BigQuery for storing vector embeddings and
>    MLTransform for computing embeddings to demonstrate an end-to-end anomaly
>    detection task on a dataset of system logs.
>
>    - *Feature Engineering & Model Evaluation
>    
> <https://github.com/apache/beam/tree/master/sdks/python/apache_beam/yaml/examples/transforms/ml/fraud_detection>*:
>    A workflow containing model training and several pipelines, showcasing an
>    end-to-end fraud detection MLOps solution that generates features and
>    evaluates models to detect credit card transaction frauds.
>
> These illustrative pipelines and workflows will be a very nice addition to
> Beam, especially with Beam 3.0 coming up. I'm also very glad to have been
> working on this larger goal of democratizing data processing for everyone.
> And as always, a huge thank you to my mentor Chamikara Jayalath and the
> larger Beam community for your support throughout this project!
>
> Best,
> Charles
>
>

Reply via email to