Building Sentence Similarity Applications at Scale
Building Sentence Similarity Applications at Scale

Abstract: 

Comparing the similarity of two sentences is an integral part of many Natural Language Processing scenarios. These scenarios range from search and retrieval, nearest-neighbor to kernel-based classification methods, recommendation, and ranking tasks. Building state of the art models at production level scale can be difficult when you’re on a small team and not both an NLP and DevOps expert. In this workshop, we will walk through the Natural Language Processing Best Practices Github Repo (https://github.com/microsoft/nlp ) provided by Microsoft on how to create baseline representation models for Sentence Similarity scenarios from popular open source technologies like gensim and scikit-learn. We will then use Microsoft's Automated Machine Learning to create a competitive model with popular sentence encoders from Google and create reusable machine learning pipelines deployed at scale on Azure Kubernetes Services.

Bio: 

Janhavi started working for Microsoft within a few months post-graduation. She has a Masters in Computer Science from Northeastern University and an undergraduate degree from University of Mumbai. After undergraduate studies, she worked for 2 years at JP Morgan Chase and Co in India and then moved to Boston for graduate studies.