Thanks for all the hard work Charles 🙂. - Cham
On Wed, Sep 24, 2025, 8:51 AM XQ Hu via dev <[email protected]> wrote: > 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 >> >>
