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: 

Courtney Cochrane is a Data Scientist in the Microsoft Artificial Intelligence Development Acceleration Program (MAIDAP). In her current role, she is responsible for accelerating the integration and development of AI models across Microsoft’s different product teams. She has previously partnered with Office, and most recently has worked with Microsoft’s Azure Machine Learning team to create a repo that showcases best practices for NLP scenarios (https://github.com/microsoft/nlp). Prior to joining Microsoft, she earned a degree in Mathematics and Computer Science from Davidson College and a Master’s in Computational Science and Engineering from Harvard University.