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: 

Abhiram is a Software Engineer at Microsoft's AI Acceleration and Development Program. His duties are similar to a machine learning engineer working at the intersection of systems and AI. Over the last year at Microsoft, he has built machine learning pipelines for applying ML in Systems and developing operationizable pipelines, applying systems to ML. Before his time at Microsoft, he was a Software Developer at InMobi before returning back to college to do masters at University of Massachusetts, Amherst.

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