Summary:<\/em><\/strong>\u00a0 Booze Allen just launched a one-stop shop for all manner of pretested DNN models.\u00a0 They\u2019re even guaranteeing price.\u00a0 This makes buying just like picking accounting, CRM, or HRIS software.\u00a0 Equally as important, it\u2019s a genius example of platform strategy to lock in customers and lock out competitors.<\/em><\/p>\n \u00a0<\/p>\n Not anymore.\u00a0 Go down to the software store, grab a COTS (commercial off the shelf) DNN for any image or text problem you may have, add a little transfer learning, and slam, bang, thank you ma\u2019am you\u2019re in production.\u00a0 DNN applications are now commodity software like accounting, HR, CRM, or MRP software.<\/p>\n If all of this sounds a little farfetched you weren\u2019t paying attention last week when Booze Allen launched its app store for AI called Modzy<\/u><\/em><\/a>.\u00a0 There are two excellent lessons here:<\/p>\n First the story about how our customers always drive development, not the other way around even in a world as exotic as deep learning.<\/p>\n \u00a0<\/p>\n Satisfying the Customer<\/strong><\/span><\/p>\n The reasons more DNN image and text classification apps are not in production are very well understood.\u00a0 From the customer\u2019s perspective they are:<\/p>\n As the customer you could buy from one of many startups who say they are good at facial recognition, car counting, NLP\/NLU, translation, or whatever other business challenge you\u2019d like to automate.\u00a0 But finding that one consultant who is actually up on all the competitors in even a narrow application field is another crap shoot.\u00a0 Chances are you\u2019re educating that consultant on your dime and the selection process will take maybe three to six months.<\/p>\n \u00a0<\/p>\n Booze Allen and Modzy.com<\/strong><\/span><\/p>\n Booze launched Modzy specifically to address these points.\u00a0 It\u2019s a:<\/p>\n Wow, if it\u2019s really that simple, why wouldn\u2019t you shop there?<\/p>\n \u00a0<\/p>\n What\u2019s in the Modzy Shop:<\/strong><\/span><\/p>\n Pretty much anything a government or commercial client could want.\u00a0\u00a0<\/p>\n Locate \/ Detect<\/strong><\/p>\n \u00a0<\/p>\n Label \/ Classify<\/strong><\/p>\n \u00a0<\/p>\n Transcribe \/ Translate<\/strong><\/p>\n \u00a0<\/p>\n Vectorize<\/strong><\/p>\n \u00a0<\/p>\n Enhance \/ Preprocess<\/strong><\/p>\n \u00a0<\/p>\n Track<\/strong><\/p>\n \u00a0<\/p>\n Count<\/strong><\/p>\n \u00a0<\/p>\n The current offering lists about 65 application but some are duplicates in different categories.\u00a0 Booze says soon there will be hundreds.<\/p>\n What you won\u2019t find in Modzy are any ML models.\u00a0 No \u2018next best offer\u2019 or recommenders.\u00a0 This is strictly focused on DNN models.\u00a0 ML implementations are already very reliable so there are few pain points to address.<\/p>\n \u00a0<\/p>\n Brilliant Platform Strategy<\/strong><\/span><\/p>\n Beyond the details of the \u2018store\u2019, this represents a really brilliant platform strategy.\u00a0<\/p>\n A Platform Strategy is technically described as a two-sided market, or two-sided network.\u00a0 The centerpiece is an intermediary economic platform with two distinct user groups, typically buyers and sellers, which adds value to the transactions by exploiting Metcalfe\u2019s law, showing that the value of the network increases with the number of users.<\/p>\n There are several key characteristics here:<\/p>\n So how does this apply to Booze and Modzy?\u00a0 First of all Booze is a well-established international consulting firm with a strong presence in both the commercial and government world.\u00a0 They don\u2019t technically control their clients but those clients will certainly sit up and take notice when Booze gets behind a movement like this.<\/p>\n Information that is essentially free is the glue that binds all this together.\u00a0 What exactly each DNN will do, how well it will do it, and how much it will cost.<\/p>\n Derisking the purchase is also a huge draw.<\/p>\n And while I have long been a proponent of always using the most accurate model to achieve the greatest possible benefit, taking all these other factors into consideration, a good-enough model could easily be the right business decision.<\/p>\n Booze has now also introduced itself as a gatekeeper to all those DNN startups that would like to access this market.\u00a0 Who exactly Booze will allow on Modzy is clearly a topic with both financial, experiential, and ease of use components.<\/p>\n \u00a0<\/p>\n Will the Other Major Platform Players Follow?<\/strong><\/span><\/p>\n Will Google, Microsoft, and the other cloud providers follow this model?\u00a0 If you\u2019re getting paid by the flop then you really want as many competitors participating as possible.\u00a0 The Modzy model looks to pick winners and losers so my guess is that cloud providers won\u2019t rush to this.<\/p>\n Other major professional services companies looking to create a tighter bond with their clients probably will.<\/p>\n And the final lesson is that if you\u2019re one of the hundreds of startup DNN shops looking for a market, you had best get on board with these platforms before you find yourself excluded.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles on AI Platform Strategy<\/strong><\/span><\/p>\n AI\/ML Lessons for Creating a Platform Strategy \u2013 Part 2<\/u><\/em><\/a><\/p>\n AI\/ML Lessons for Creating a Platform Strategy \u2013 Part 1<\/u><\/em><\/a><\/p>\n A Radical AI Strategy – Platformication<\/u><\/em><\/a><\/p>\n Now that We\u2019ve Got AI What do We do with It?<\/u><\/em><\/a><\/p>\n Capturing the Value of ML\/AI \u2013 the Challenge of Offensive versus Defensive Data Strategies<\/u><\/em><\/a><\/p>\n The Case for Just Getting Your Feet Wet with AI<\/u><\/em><\/a><\/p>\n The Fourth Way to Practice Data Science \u2013 Purpose Built Analytic Modules<\/u><\/em><\/a><\/p>\n From Strategy to Implementation \u2013 Planning an AI-First Company<\/u><\/em><\/a><\/p>\n Comparing the Four Major AI Strategies<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Systems of Intelligence<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Vertical versus Horizontal.<\/u><\/em><\/a><\/p>\n What Makes a Successful AI Company<\/u><\/em><\/a> \u2013 Data Dominance<\/u><\/em><\/span><\/p>\n AI Strategies \u2013 Incremental and Fundamental Improvements<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill is Contributing Editor for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0 His articles have been read more than 2 million times.<\/p>\n He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: William Vorhies Summary:\u00a0 Booze Allen just launched a one-stop shop for all manner of pretested DNN models.\u00a0 They\u2019re even guaranteeing price.\u00a0 This makes buying […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":473,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2810"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2810"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2810\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/467"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2810"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2810"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2810"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>The common vision of developing and deploying a deep learning model is half-a-dozen (at least) data scientists and engineers slogging away over maybe three to six months before having that MVP to first test in production.<\/p>\n
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<\/a>Companies adopting platform strategies are racing to the front.<\/p>\n
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