Machine learning has become a core technology underlying many modern applications, especially utilizing natural language processing, where the techniques provide powerful methods for analyzing large data sets, such as contracts, electronic health records, social interactions, and other unstructured text data. With the ability for recent powerful techniques to retain meaning, search, and perform machine translation at high fidelity, alongside many open-source traditional and hybrid methods, transforming unstructured content to structured insights, events, and relationships is at the fingertips. Organizations are looking to leverage these emerging technologies and close capability gaps to ingest, monitor, error-check, automate, or improve their capabilities in processing and understanding hundreds of millions of documents. While certain tasks are well addressed by existing systems, organizations often still struggle with implementation, identification of the correct methods & algorithms, as well as properly scale their models to solve open challenges within their terminology.  In this session, we examine the data strategy and technical use cases involving natural language processing, the algorithms appropriate for certain project objectives, and discuss the development and deployment of these solutions.


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Session Overview

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    ODSC East 2020: Developing Natural Language Processing Pipelines for Industry

    • Overview and Author Bio

    • Developing Natural Language Processing Pipelines for Industry

Instructor Bio:

Michael Luk, PhD

Michael Luk has more than 10 years of experience in developing and delivering hybrid product and service solutions to life sciences and healthcare clients. As the CTO of SFL Scientific, he focuses on managing and developing business operations from start-ups to multi-billion dollar companies. Michael has provided clients with innovative, practical solutions by improving operations and integrating technology through the development of novel data-driven systems. Michael Luk is an expert in machine learning and AI and has vast experience in time-series modeling. He studied theoretical physics at Imperial College London, Mathematics at the University of Cambridge before completing his doctorate in Particle Physics at Brown University. SFL Scientific is a turn-key data science consulting firm that offers custom development and solutions in data engineering, machine learning, and predictive analytics. SFL uses specific domain knowledge, innovation, and latest technical advances to solve complex and novel business problems 

Michael Luk, PhD

Chief Technology Officer | SFL Scientific