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Natural Language Processing [clear filter]
Wednesday, September 30
 

2:35pm PDT

Easy Access to SOTA NLP Models with Ray and Hugging Face - Thomas Wolf, Hugging Face
In this talk, I'll discuss how NLP researchers and practitioners can leverage Hugging Face models and datasets libraries together with Ray distributed tools to use and train the latest state-of-the-art NLP models.

Speakers
avatar for Thomas Wolf

Thomas Wolf

Co-founder and Chief Science Officer, Hugging Face
Thomas Wolf is co-founder and Chief Science Officer of Hugging Face. His team is on a mission to catalyze and democratize NLP research. Prior to HuggingFace, Thomas gained a Ph.D. in physics, and later a law degree. He worked as a physics researcher and a European Patent Attorney... Read More →



Wednesday September 30, 2020 2:35pm - 3:05pm PDT
Virtual 2
 
Thursday, October 1
 

11:45am PDT

Turbocharging State-of-the-art Natural Language Processing on Ray - David Talby, John Snow Labs
This session introduces the Python nlu library, which provides the full power of Spark NLP as simple Python one-liners that directly read and write data frames. We will walk through some of the 250+ pre-trained NLP models & pipelines that come with the library. We'll then describe how the nlu library integrates Ray and Spark NLP to enable you to get the performance, scale, and accuracy benefits of both without having to learn new API's or implementation details.

Speakers
avatar for David Talby

David Talby

Chief Technology Officer, John Snow Labs
David Talby is a chief technology officer at John Snow Labs, the creators of Spark NLP: a production-grade, fast & trainable implementation of the latest research in natural language processing. David specializes in building & operating AI systems in healthcare and life science, and... Read More →



Thursday October 1, 2020 11:45am - 12:15pm PDT
Virtual 3

4:50pm PDT

Building Complex Data Analytics Pipelines with Ray - Qingqing Mao, Dascena
Building scalable data analytics pipelines is challenging, especially when different subtasks may have different computational requirements and interdependencies. It becomes more challenging when you need to serve enterprise customers who have strict data security and privacy policies and require on-premise deployment. The scaling requirement and computational capacity often vary widely from site to site.

We have been using Ray to build natural language processing pipelines and healthcare analysis pipelines. The highly efficient serialization using a shared-memory object store is a perfect fit for handling our data-intensive jobs. Ray helps us narrow the gap between data science and engineering, and it enables our data scientists to write high-performance and cost-efficient data analytics pipelines that can scale. 

Speakers
avatar for Qingqing Mao

Qingqing Mao

Head of Engineering and Data Science, Dascena
Qingqing Mao is the Head of Engineering and Data Science at Dascena, where he leads the development of compliant and scalable clinical data pipelines and the research on applying machine learning techniques in healthcare and medicine. Previously, he worked as a senior staff data scientist... Read More →



Thursday October 1, 2020 4:50pm - 5:20pm PDT
Virtual 2
 
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