Scaling Embedding Generation Pipelines From Pandas to Ray Data
Blog post from Anyscale
This blog post explores scaling up a pipeline that generates text embeddings using Ray Data and Sentence Transformers. The author demonstrates an easy migration from a pandas-based pipeline to a Ray Data-based pipeline, highlighting significant performance improvements with minimal code changes. The improved Ray Data pipeline delivers a 10x performance improvement over the naive implementation and allows for distribution of workload across a cluster of machines with GPUs and CPUs compared to running pandas on a single machine.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 36 | 3,701 | 290 | 90 | +59% |
| Data Pipeline | 4 | 1,437 | 344 | 74 | +109% |
| RAG | 2 | 1,966 | 260 | 82 | -21% |
| LLM | 1 | 4,030 | 486 | 147 | +1% |
| Real-time | 1 | 4,377 | 976 | 225 | +49% |
| Serverless | 1 | 676 | 180 | 85 | +28% |
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