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Learn how the open-source Spark NLP library provides optimized and scalable LLM inference for high-volume text and image processing pipelines. This session dives into optimized LLM inference without the overhead of commercial APIs or extensive hardware setups. We will show live code examples and benchmarks comparing Spark NLP’s performance and cost-effectiveness against both commercial APIs and other open-source solutions.

Key Takeaways:

  • Learn how to efficiently process millions of LLM interactions daily, circumventing the costs associated with traditional LLM deployments.
  • Discover advanced methods for embedding LLM inference within existing data processing pipelines, enhancing throughput and reducing latency.
  • Review benchmarks that compare Spark NLP’s speed and cost metrics relative to commercial and open-source alternatives.
Danilo Burbano
Software and Machine Learning Engineer at John Snow Labs

Danilo Burbano is a Software and Machine Learning Engineer at John Snow Labs. He holds an MSc in Computer Science and has 13 years of commercial experience.

He has previously developed several software solutions over distributed system environments like microservices and big data pipelines across different industries and countries. Danilo has contributed to Spark NLP for the last 6 years. He is now working to maintain and evolve the Spark NLP library by continuously adding state-of-the-art NLP tools to allow the community to implement and deploy cutting-edge large-scale projects for AI and NLP.

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