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Building Reproducible Evaluation Processes for Spark NLP Models

Healthcare organizations can face numerous challenges when developing high-quality machine learning models. Data is often noisy and unstructured, and developing successful models involves experimenting with numerous parameter configurations, datasets, and model types. Tracking and analyzing the results of these variations can quickly become a huge challenge as the size of an ML team grows.

When building models in this environment, you must iterate fast and frequently, while preserving transparency into your process. The ability to do this can be limited by your choice of tools. In this session, Comet Data Scientist, Dhruv Nair will share how Spark NLP users can leverage the integration with Comet’s ML development platform to create robust evaluation processes for NLP models. You will learn how to use these tools to enhance team collaboration, model reproducibility, and experimentation velocity.

By the end of this session, you will understand how to track your experiments, create visibility into your model development process, and share results and progress with your team.

John Snow Labs’ Spark NLP for Healthcare Library Speeds Up Automated Language Processing with Intel® AI Technologies

Advances and breakthroughs in medicine and public health are built on research and prior learnings. Understandings are contained in a wide range of...