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


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 ( ) 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.


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.

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