{"id":48803,"date":"2021-11-03T16:45:47","date_gmt":"2021-11-03T11:15:47","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/we-have-used-nlp-techniques-to-automate-search-results\/"},"modified":"2024-09-03T08:42:29","modified_gmt":"2024-09-03T03:12:29","slug":"we-have-used-nlp-techniques-to-automate-search-results","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/we-have-used-nlp-techniques-to-automate-search-results\/","title":{"rendered":"We have used NLP techniques to automate search results - Prashant Malavade, PGP AIML"},"content":{"rendered":"
\"aiml\"<\/figure>\n\n\n\n

\"A new search engine was developed, which will be integrated into existing tools. With this new AI, tool search time is reduced from hours to a few minutes.\", says Prashant Malavade. Similarly, you can implement AI concepts and optimize workflow as well. Read further to learn about Prashant Malavade's journey with Great Learning's PGP Artificial Intelligence and Machine Learning Course<\/a> in his own words. <\/em><\/p>\n\n\n\n

I am in the field of designing products for the Turbomachinery domain. One of my responsibilities involves providing resolution to non-conformity arising from manufacturing or supplier during the production of parts. The typical timeline to close any non-conformity request is 2-3 days as it involves a detailed study of deviation and its impact on the functionality of the overall system. Most of the time, we get repetitive deviations of similar nature on the same part or on other parts, and for such cases, resolutions are already available in the tool. But as the current process is manual, we spend more time searching for similar non-conformities in the tool database. Another challenge is, major parts of historical data are in Italian, which needs to be translated to understand deviation. I planned to automate the search by applying AI techniques.<\/p>\n\n\n\n

We have used NLP techniques to automate search results. To solve the language-translation problem, we have used a machine translation based on the BERT model. For performing similarity search using BERT embedding to get vector representation and then applying similarity search using cosine distance for embedded vectors tried to rank top 10 similar non-conformities solved in the past.<\/p>\n\n\n\n

All relevant data used to build AI search tools was already available in existing tools. Most of the data are in English text as well as in Italian text. The model was built and trained on the Keras platform with GPU options. Model accuracy was in the tune of 90%, which is sufficient for our need as the search model provided a top 10 list of non-conformities.<\/p>\n\n\n\n

A new search engine was developed, which will be integrated into existing tools. With this new AI, tool search time is reduced from hours to a few minutes. We are able to provide disposition within minutes instead of days which will result in a better production schedule and delivery of product on time. For us, we will be able to spend more time on our core activities.<\/p>\n","protected":false},"excerpt":{"rendered":"

\"A new search engine was developed, which will be integrated into existing tools. With this new AI, tool search time is reduced from hours to a few minutes.\", says Prashant Malavade. Similarly, you can implement AI concepts and optimize workflow as well. 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