In this liveProject, you’ll bring together multiple tools and models to construct a production-grade smart search engine. You’ll combine off-the-shelf Elasticsearch models for keyword search with your own semantic search API using transformers. Start by setting up and indexing an Elasticsearch Docker container, then quickly move on to boosting search relevance with BERT. Finally, you’ll set up a Flask API to serve a BERT model and look into customizing your search engine for your own particular topics of interest.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
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project author
Olesya Bondarenko
Olesya Bondarenko has a multidisciplinary background and experience in natural language processing (NLP), machine learning, deep learning, statistics, time-series analysis, process automation, engineering R&D and new product prototyping. Currently, she is a data scientist at Strong Analytics, a leading provider of customized AI solutions, where she specializes in developing NLP systems. Prior to joining Strong, she worked with several startups leading research and development efforts in the areas of conversational AI, data-leveraged scientific discovery solutions, and a variety of automated analytic and data collection tools. Olesya received her PhD in electrical engineering from the University of California San Diego where she designed and prototyped novel optical devices, as well as custom instrumentation for their analysis.
prerequisites
This liveProject is for intermediate Python programmers familiar with the command line and Docker. To begin this liveProject you will need to be familiar with:
TOOLS
Intermediate Python
Basic understanding of conda environments
Basic Docker
Basic Flask web development
TECHNIQUES
Reading data from and writing to JSON files
Manipulations with tuples, lists, and dictionaries using loops and comprehensions
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