{"id":33169,"date":"2023-08-23T05:00:00","date_gmt":"2023-08-23T12:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33169"},"modified":"2023-08-18T15:09:57","modified_gmt":"2023-08-18T22:09:57","slug":"survey-more-than-75-of-enterprises-dont-plan-to-use-commercial-llms-in-production-citing-data-privacy-as-primary-concern","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/08\/23\/survey-more-than-75-of-enterprises-dont-plan-to-use-commercial-llms-in-production-citing-data-privacy-as-primary-concern\/","title":{"rendered":"Survey: More than 75% of Enterprises Don\u2019t Plan to Use Commercial LLMs in Production Citing Data Privacy as Primary Concern\u00a0"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignright size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"246\" height=\"78\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/05\/Predibase_logo.png\" alt=\"\" class=\"wp-image-29337\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/05\/Predibase_logo.png 246w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2022\/05\/Predibase_logo-150x48.png 150w\" sizes=\"(max-width: 246px) 100vw, 246px\" \/><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/predibase.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Predibase<\/a>, the commercially available low-code declarative ML platform for developers, today released a new report, \u201c<a href=\"https:\/\/21944583.fs1.hubspotusercontent-na1.net\/hubfs\/21944583\/eBooks\/Predibase_LLMs_in_Production_Survey_Whitepaper_2023.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Beyond the Buzz: A Look at Large Language Models in Production<\/a>.\u201d Based on survey data from organizations experimenting with LLMs, the report offers insight into real-world concerns, opportunities, and priorities for organizations as they embrace AI and LLMs. Among the key findings: enterprises are looking for ways to customize and deploy open-source LLMs without giving commercial vendors access to proprietary data, and they are exploring other use cases beyond generative AI capabilities.\u00a0\u00a0<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201cIt is now open season for Large Language Models (LLMs). Thanks to the widespread recognition of OpenAI\u2019s ChatGPT, businesses are in an arms race to gain a competitive edge using the latest AI capabilities. Still, they require more customized LLMs to meet domain-specific use cases,\u201d said Piero Molino, co-founder and CEO of Predibase. \u201cThis report highlights the need for the industry to focus on the real opportunities and challenges as opposed to blindly following the hype.\u201d&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<p>The report highlights emerging trends from LLMs in production using responses from 150 executives, data scientists, machine learning engineers, developers, and product managers at both large and small enterprises across 29 countries. Key findings include:<\/p>\n\n\n\n<ul>\n<li><strong>Less than a quarter of enterprises are comfortable using commercial LLMs.<\/strong>&nbsp; Roughly a third (33%) cite concerns about sharing sensitive or proprietary data with commercial LLM vendors, leading to increased interest in privately hosted, open-source alternatives.&nbsp;<\/li>\n\n\n\n<li><strong>Open-source LLMs are gaining momentum.&nbsp;<\/strong>Nearly 77% of respondents either don\u2019t use or don\u2019t plan to use commercial LLMs beyond prototypes in production, citing concerns about privacy, cost, and lack of customization, leading to an uptick in open-source alternatives. Meta, for example, has moved away from building closed-source LLMs like LLaMA-1, replacing it with LLaMA-2, available as open-source and free for commercial and research applications.&nbsp;<\/li>\n\n\n\n<li><strong>While generative AI use cases remain popular, enterprises see the potential of other applications to provide business value.<\/strong>&nbsp;Information Extraction is the second most popular use case (selected by 32.6% of respondents). This involves leveraging LLMs to convert unstructured data like PDF documents or customer emails into structured tables for aggregate analytics. Next was Q&amp;A and Search (15.2% of respondents), the brain in chatbots that provides accurate and relevant responses to user queries in real-time.<\/li>\n\n\n\n<li><strong>Organizations are turning to customized LLMs to achieve more accurate and tailored results.<\/strong>&nbsp;Most teams plan to customize their LLMs by fine-tuning (32.4%) or reinforcement learning with human feedback (27%). The roadblocks team face with fine-tuning continue to be a lack of data (21%) and the overall complexity of the process like managing infrastructure (46%).<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201cWe see clear potential to improve the outcomes of our conservation efforts using customized open-source LLMs to help our teams generate insights and learnings from our large corpus of project reports,&#8221; said\u00a0Dave Thau, Global Data and Technology Lead Scientist, World Wildlife Fund.<\/p>\n<\/blockquote>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>\u201cClearly, companies are investing in the personnel and technologies necessary to work with emerging generative AI technologies to support production-scale outcomes,\u201d added\u00a0Bradley Shimmin, Chief Analyst AI platforms, analytics, and data management at Omdia. \u201cThe trick, of course, will rest not in building these outcomes but in ensuring that they deliver consistent, secure, responsible outcomes. With an increasing desire to customize and deploy open-source models, enterprises will need to invest in operational tooling and infrastructure capable of keeping up with the rapid pace of innovation in the open-source community.\u201d<\/p>\n<\/blockquote>\n\n\n\n<p>To download the entire \u201cBeyond the Buzz: A Look at Large Language Models in Production\u201d report, click <a href=\"https:\/\/21944583.fs1.hubspotusercontent-na1.net\/hubfs\/21944583\/eBooks\/Predibase_LLMs_in_Production_Survey_Whitepaper_2023.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">HERE<\/a>. <\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn:&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook:&nbsp;<a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predibase, the commercially available low-code declarative ML platform for developers, today released a new report, \u201cBeyond the Buzz: A Look at Large Language Models in Production.\u201d Based on survey data from organizations experimenting with LLMs, the report offers insight into real-world concerns, opportunities, and priorities for organizations as they embrace AI and LLMs.<\/p>\n","protected":false},"author":10513,"featured_media":32680,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,182,180,67,268,56,84,1],"tags":[437,1254,1248,968,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - 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