MLOps Spotlight: Scaling NLP Pipelines at IHS Markit


The data science team at IHS Markit has been hard at work building sophisticated NLP pipelines that work at scale using the Iguazio MLOps platform and open source MLRun framework. Today they will share their journey and provide advice for other data science teams looking to:

Ingest, prepare, classify and index structured and unstructured data (in this case, PDFs and Images)
Handle terabytes of data in hours, not months
Work in one unified research and production environment to make deployment seamless
Enable CI/CD for ML
Run complex models that make unstructured data searchable (including computer vision)
Allow for sharing and reuse of components across projects and teams
Utilize auto-scaling serverless functions to abstract away infrastructure complexities
Build rapidly, iterate faster and focus on the business logic and not the underlying infrastructure

Nick (IHS Markit) and Yaron (Iguazio) will share their approach to automating the NLP pipeline end to end. They’ll also provide details on leveraging capabilities such as Spot integration and Serving Graphs to reduce costs and improve the data science process.


Nick Brown is a Senior Data Scientist at IHS Markit working within the Engineering and Product Design business line. Currently working in AI based information extraction, Nick has worked on projects which generated millions of dollars of incremental value for organizations by applying data science and machine learning to pricing and purchasing optimization, competitive behavior analysis, and geographic demand seasonality. He is dedicated to educating people unfamiliar with data science to facilitate long-term success both within his organization and in the greater business community.

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