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 | 3,717 | 293 | 93 | +59% |
| Data Pipeline | 4 | 1,437 | 344 | 74 | +108% |
| RAG | 2 | 1,977 | 263 | 84 | -21% |
| LLM | 1 | 4,111 | 492 | 149 | +2% |
| Real-time | 1 | 4,428 | 995 | 231 | +49% |
| Serverless | 1 | 683 | 181 | 87 | +29% |
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