{"id":302051,"date":"2021-07-18T18:00:38","date_gmt":"2021-07-18T15:00:38","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/why-90-of-machine-learning-models-never-hit-the-market\/"},"modified":"2021-07-18T18:00:38","modified_gmt":"2021-07-18T15:00:38","slug":"why-90-of-machine-learning-models-never-hit-the-market","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/","title":{"rendered":"#Why 90% of machine learning models never hit the market"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a30bba84193b\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #dd3333;color:#dd3333\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #dd3333;color:#dd3333\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a30bba84193b\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Corporations_arent_set_up_for_machine_learning\" >Corporations aren\u2019t set up for machine learning<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Leadership_support_means_more_than_money\" >Leadership support means more than money<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Lacking_access_to_data\" >Lacking access to data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#The_disconnect_between_IT_data_science_and_engineering\" >The disconnect between IT, data science, and engineering<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Machine_learning_models_come_with_their_own_set_of_challenges\" >Machine learning models come with their own set of challenges<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Scaling_up_is_harder_than_you_think\" >Scaling up is harder than you think<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Efforts_get_duplicated\" >Efforts get duplicated<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Execs_dont_always_buy_in\" >Execs don\u2019t always buy in<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Lack_of_cross-language_and_framework_support\" >Lack of cross-language and framework support<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#Versioning_and_reproducibility_remain_challenging\" >Versioning and reproducibility remain challenging<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#How_to_stop_trying_and_start_deploying\" >How to stop trying and start deploying<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/buradabiliyorum.com\/en\/why-90-of-machine-learning-models-never-hit-the-market\/#The_bottom_line_revolutions_take_time\" >The bottom line: revolutions take time<\/a><\/li><\/ul><\/nav><\/div>\n<p>&#8220;<strong>#Why 90% of machine learning models never hit the market<\/strong>&#8221;<\/p>\n<div>Corporations are going through rough times. And I\u2019m not talking about the pandemic and the stock market volatility.<\/p>\n<p>The times are uncertain, and having to make customer experiences more and more seamless and immersive isn\u2019t taking off any of the pressure on companies. In that light, it\u2019s understandable that they\u2019re pouring billions of dollars into the development of machine learning models to improve their products.<\/p>\n<p>But there\u2019s a problem. Companies can\u2019t just throw money at data scientists and machine learning engineers, and hope that magic h<a href=\"https:\/\/buradabiliyorum.com\/en\/category\/download-scripts-themes-apps\/\" data-internallinksmanager029f6b8e52c=\"9\" title=\"Download Scripts &amp; Themes &amp; Apps\" target=\"_blank\" rel=\"noopener\">app<\/a>ens.<\/p>\n<p>The data speaks for itself. As <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/07\/19\/why-do-87-of-data-science-projects-never-make-it-into-production\/\">VentureBeat reported last year<\/a>, around 90 percent of machine learning models never make it into production. In other words, only one in ten of a data scientist\u2019s workdays actually end up producing something useful for the company.<\/p>\n<p>Even though <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/03\/11\/edelman-91-of-tech-execs-believe-mundane-tasks-will-be-relegated-to-machines\/\">9 out of 10<\/a>\u00a0tech executives believe that AI will be at the center of the next technological revolution, its adoption and deployment leave room for growth. And the data scientists aren\u2019t the ones to blame.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Corporations_arent_set_up_for_machine_learning\"><\/span>Corporations aren\u2019t set up for machine learning<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Leadership_support_means_more_than_money\"><\/span>Leadership support means more than money<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The job market for data scientists is <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/12\/11\/algorithmia-50-of-companies-spend-upwards-of-three-months-deploying-a-single-ai-model\/\">pretty great<\/a>. Companies are hiring, and they\u2019re ready to pay a good salary, too.<\/p>\n<p>Of course, managers and corporate leaders expect from these data scientists that they add a lot of value in return. For the moment, however, they\u2019re not making it easy to do so.<\/p>\n<p>\u201cSometimes people think, all I need to do is throw money at a problem or put a <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/technology\/\" data-internallinksmanager029f6b8e52c=\"4\" title=\"Technology\" target=\"_blank\" rel=\"noopener\">technology<\/a> in, and success comes out the other end,\u201d says <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/07\/19\/why-do-87-of-data-science-projects-never-make-it-into-production\/\">Chris Chapo<\/a>, SVP of data and analytics at GAP.<\/p>\n<p>To help data scientists excel in their roles, leaders don\u2019t only need to direct resources in the right direction, but also understand what machine learning models are all about. One possible solution is that leaders get some introductory training to data <a href=\"https:\/\/buradabiliyorum.com\/en\/category\/sciencee\/\" data-internallinksmanager029f6b8e52c=\"5\" title=\"Science\" target=\"_blank\" rel=\"noopener\">science<\/a> themselves, so they can put this knowledge into practice at their companies.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Lacking_access_to_data\"><\/span>Lacking access to data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Companies aren\u2019t bad at collecting data. However, many companies are highly siloed, which means that each department has its own ways of collecting data, preferred formats, places where they store it, and security and privacy preferences.<\/p>\n<p>Data scientists, on the other hand, often need data from several departments. Siloing makes it harder to clean and process that data. Moreover, many data scientists <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/07\/19\/why-do-87-of-data-science-projects-never-make-it-into-production\/\">complain<\/a> that they can\u2019t even obtain the data they need. But how should you even start training a model if you don\u2019t have the necessary data?<\/p>\n<p>Siloed company structures \u2014 and inaccessible data \u2014 might have been manageable in the past. But in an era where technological transformation is happening at breakneck speed, companies will need to step up and set up uniform data structures throughout.<\/p>\n<figure class=\"post-image post-mediaBleed aligncenter\"><img loading=\"lazy\" decoding=\"async\" alt=\"Woman sitting in front of computer screen which shows the words \u201ccode is beautiful\u201d\" width=\"1225\" height=\"817\" class=\"js-lazy\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*6eAFY8P6oI_s82vOoDDr3A.jpeg\"\/><figcaption><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/thenextweb.com\/news\/#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F07%2F18%2Fwhy-most-machine-learning-models-never-hit-market-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: For data scientists to do their job, it\u2019s vital that they get access to the data they need. Image by author\" data-title=\"Share For data scientists to do their job, it\u2019s vital that they get access to the data they need. Image by author on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share For data scientists to do their job, it\u2019s vital that they get access to the data they need. Image by author on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"\/><\/a>For data scientists to do their job, it\u2019s vital that they get access to the data they need. Image by author<\/figcaption><noscript><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*6eAFY8P6oI_s82vOoDDr3A.jpeg\" alt=\"Woman sitting in front of computer screen which shows the words \u201ccode is beautiful\u201d\" width=\"1225\" height=\"817\" class=\"\" srcset=\"\"\/><\/noscript><\/figure>\n<h3><span class=\"ez-toc-section\" id=\"The_disconnect_between_IT_data_science_and_engineering\"><\/span>The disconnect between IT, data science, and engineering<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If companies aim to get less siloed, that also means that departments need to communicate more with one another and align their goals.<\/p>\n<p>In many companies, there\u2019s a fundamental <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.redapt.com\/blog\/why-90-of-machine-learning-models-never-make-it-to-production#:~:text=During%20a%20panel%20at%20last,actually%20make%20it%20into%20production.\">divide<\/a> between the IT and data science departments. IT tends to prioritize making things work and keeping them stable. Data scientists, on the other hand, like experimenting and breaking things. This doesn\u2019t lead to effective communication.<\/p>\n<p>In addition, engineering isn\u2019t always deemed essential for data scientists. This is a problem because engineers might not always understand all the details of what a data scientist envisions, or might implement things differently due to miscommunication. Therefore, data scientists who can deploy their models have a competitive edge over those who can\u2019t, as <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/stackoverflow.blog\/2020\/10\/12\/how-to-put-machine-learning-models-into-production\/?utm_source=Iterable&amp;utm_medium=email&amp;utm_campaign=the_overflow_newsletter\">StackOverflow<\/a> points out.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Machine_learning_models_come_with_their_own_set_of_challenges\"><\/span>Machine learning models come with their own set of challenges<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Scaling_up_is_harder_than_you_think\"><\/span>Scaling up is harder than you think<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If a model works great in a small environment, that doesn\u2019t imply that it\u2019ll work everywhere.<\/p>\n<p>For one, the hardware or cloud storage space to handle bigger datasets might not be available. In addition, modularity of machine learning models doesn\u2019t always work the same at large scales as it does on small ones.<\/p>\n<p>Finally, data sourcing may not be easy or even possible. This can be due to silo-structures in companies, as discussed earlier, or due to other challenges in obtaining more data.<\/p>\n<p>This is yet another reason to unify data structures across organizations, and encourage communication between different departments.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Efforts_get_duplicated\"><\/span>Efforts get duplicated<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>On the long road to deploying machine learning models, more than a quarter of all companies face duplicated efforts.<\/p>\n<p>For example, a software engineer might try to implement what a data scientist told them to. The latter might go ahead and do some of the work themselves, too.<\/p>\n<p>Not only is this a waste of time and resources. It can also lead to additional confusion when stakeholders don\u2019t know which version of the code to use, and who to turn to if they encounter any bugs.<\/p>\n<p>Although data scientists have an advantage if they\u2019re able to implement their models, they should clearly communicate with the engineers about what needs to be done by whom. This way, they\u2019ll save the company\u2019s time and resources.<\/p>\n<figure class=\"post-image post-mediaBleed aligncenter\"><img loading=\"lazy\" decoding=\"async\" alt=\"One man and two women sitting and talking at table with a laptop on it\" width=\"1225\" height=\"817\" class=\"js-lazy\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*0fc5RzMzeH-pYSUiVrOjaA.jpeg\"\/><figcaption><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/thenextweb.com\/news\/#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F07%2F18%2Fwhy-most-machine-learning-models-never-hit-market-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: Effective communication is vital to make machine learning models work. Image by author\" data-title=\"Share Effective communication is vital to make machine learning models work. Image by author on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share Effective communication is vital to make machine learning models work. Image by author on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"\/><\/a>Effective communication is vital to make machine learning models work. Image by author<\/figcaption><noscript><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*0fc5RzMzeH-pYSUiVrOjaA.jpeg\" alt=\"One man and two women sitting and talking at table with a laptop on it\" width=\"1225\" height=\"817\" class=\"\" srcset=\"\"\/><\/noscript><\/figure>\n<h3><span class=\"ez-toc-section\" id=\"Execs_dont_always_buy_in\"><\/span>Execs don\u2019t always buy in<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Tech executives <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/venturebeat.com\/2019\/03\/11\/edelman-91-of-tech-execs-believe-mundane-tasks-will-be-relegated-to-machines\/\">strongly believe<\/a> in the power of AI as a whole, but that doesn\u2019t mean that they\u2019re convinced by every idea out there. As <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/info.algorithmia.com\/hubfs\/2019\/Whitepapers\/The-State-of-Enterprise-ML-2020\/Algorithmia_2020_State_of_Enterprise_ML.pdf\">Algorithmia<\/a> reports, a third of all business executives blame the poor deployment statistics on a lack of senior buy in.<\/p>\n<p>It seems as if data scientists are still viewed as somewhat nerdy and devoid of business sense. This makes it all the more important that data scientists amp up their business skills and seek the dialog with senior execs whenever possible.<\/p>\n<p>Of course, that doesn\u2019t mean that every data scientist suddenly needs an MBA to excel at their job. However, some key learnings from classes or business experience might serve them a long way.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Lack_of_cross-language_and_framework_support\"><\/span>Lack of cross-language and framework support<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Since machine learning models are still in their infancy, there are still considerable gaps when it comes to different languages and frameworks.<\/p>\n<p>Some pipelines start in Python, continue in R, and end in Julia. Others go the other way around, or use other languages entirely. Since each language comes with unique sets of libraries and dependencies, projects quickly get hard to keep track of.<\/p>\n<p>In addition, some pipelines might make use of containerization with Docker and Kubernetes, others might not. Some pipelines will deploy specific APIs, others not. And the list goes on.<\/p>\n<p>Tools like TFX, Mlflow, and Kubeflow are starting to emerge to fill this gap. But these tools are still in their infancy, and expertise in them is rare as of now.<\/p>\n<p>Data scientists know that they need to keep checking out the newest developments in their field. This should apply to model deployment as well.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Versioning_and_reproducibility_remain_challenging\"><\/span>Versioning and reproducibility remain challenging<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Connected with the above issue is that there is, as of now, no go-to way of versioning machine learning models. It\u2019s quite obvious that data scientists need to keep track of any changes they make, but that\u2019s quite cumbersome these days.<\/p>\n<p>In addition, datasets may drift over time. That\u2019s natural as companies and projects evolve, but it makes it harder to reproduce past results.<\/p>\n<p>It\u2019s all the more important that as soon as a project is started, a benchmark is established against which the model runs now and in the future. In combination with diligent version control, data scientists can get their models reproducible.<\/p>\n<figure class=\"post-image post-mediaBleed aligncenter\"><img loading=\"lazy\" decoding=\"async\" alt=\"Doctor holding stethoscope to computer screen depicting lines of code\" width=\"1225\" height=\"817\" class=\"js-lazy\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*OuWK2jzKr_zcHuD4gMWSjw.jpeg\"\/><figcaption><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/thenextweb.com\/news\/#\" data-url=\"https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Feditorial.thenextweb.com%2Fneural%2F2021%2F07%2F18%2Fwhy-most-machine-learning-models-never-hit-market-syndication%2F&amp;via=thenextweb&amp;related=thenextweb&amp;text=Check out this picture on: If a model isn\u2019t reproducible, this could lead to lengthy investigations later on. Image by author\" data-title=\"Share If a model isn\u2019t reproducible, this could lead to lengthy investigations later on. Image by author on Twitter\" data-width=\"685\" data-height=\"500\" class=\"post-image-share popitup\" title=\"Share If a model isn\u2019t reproducible, this could lead to lengthy investigations later on. Image by author on Twitter\"><i class=\"icon icon--inline icon--twitter--dark\"\/><\/a>If a model isn\u2019t reproducible, this could lead to lengthy investigations later on. Image by author<\/figcaption><noscript><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/miro.medium.com\/max\/1225\/1*OuWK2jzKr_zcHuD4gMWSjw.jpeg\" alt=\"Doctor holding stethoscope to computer screen depicting lines of code\" width=\"1225\" height=\"817\" class=\"\" srcset=\"\"\/><\/noscript><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"How_to_stop_trying_and_start_deploying\"><\/span>How to stop trying and start deploying<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If 90 percent of a data scientist\u2019s efforts lead to nothing, that\u2019s not a good sign. This isn\u2019t the fault of data scientists, as shown above, but rather due to inherent and organizational obstacles.<\/p>\n<p>Change doesn\u2019t come from one day to the next. For companies who are just getting started in machine learning models, it\u2019s therefore advisable to start with a really small and simple project.<\/p>\n<p>Once managers have outlined a clear and simple project, the second step is to choose the right team. It should be cross-functional, and should include data scientists, engineers, DevOps, and any other roles that seem important for its success.<\/p>\n<p>Third, managers should consider leveraging third parties to help them accelerate at the beginning. IBM is among the companies that offer such a service, but there are others on the market, too.<\/p>\n<p>A final caveat is not to strive for sophistication at all costs. If a cheap and simple model fulfills 80 percent of customer needs and could be shipped within a couple of months, that\u2019s already a great feat. Moreover, the learnings of building the simple model will fuel the implementation of a more sophisticated model that, hopefully, makes customers 100 percent satisfied.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_bottom_line_revolutions_take_time\"><\/span>The bottom line: revolutions take time<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The next decade is bound to be revolutionary \u2014 just like the last one was. The widespread adoption of artificial intelligence is only one of many growing trends. The rise of the internet of things, advanced robotics, and blockchain technology count to this list, too.<\/p>\n<p>I\u2019m deliberately speaking of decades and not years, though. For example, consider that 90 percent of companies are in the cloud \u2014 so many that it\u2019s hard to even think about how our lives would be without it. On the flip side, clouds took several decades to gain widespread adoption.<\/p>\n<p>There\u2019s no reason to believe that the AI revolution should be any different. It will take a while to implement because the status quo contains a host of obstacles to tackle.<\/p>\n<p>But since machine learning offers so many ways to improve customer experience and corporate efficiency, it\u2019s clear that the winners will be those that deploy models fast and early.<\/p>\n<p><em>This article was written by<span>\u00a0<\/span><span data-sheets-value=\"{\" moutafis=\"\" data-sheets-userformat=\"{\">Rhea Moutafis\u00a0<\/span>and was originally published on<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>. You can read it<span>\u00a0<\/span><a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/towardsdatascience.com\/why-90-percent-of-all-machine-learning-models-never-make-it-into-production-ce7e250d5a4a\">here<\/a>.\u00a0<\/em><\/p>\n<\/div>\n<p><script async src=\"\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<blockquote><p><strong><span style=\"color: #ff6600;\">If you liked the article, do not forget to share it with your friends. Follow us on\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" href=\"https:\/\/news.google.com\/publications\/CAAqBwgKMLG0nwswvr63Aw\" target=\"_blank\" rel=\"nofollow noopener noreferrer\">Google News<\/a><\/span>\u00a0too, click on the star and choose us from your favorites.<\/span><\/strong><\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\">For forums sites go to <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/forum.buradabiliyorum.com\/\" target=\"_blank\" rel=\"noopener\">Forum.BuradaBiliyorum.Com<\/a><\/span><\/strong>\n<\/p><\/blockquote>\n<blockquote>\n<p style=\"text-align: center;\"><strong>If you want to read more like this article, you can visit our <span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" href=\"https:\/\/en.buradabiliyorum.com\/technology\/\" target=\"_blank\" rel=\"noopener\">Technology category.<\/a><\/span><\/strong><\/p>\n<\/blockquote>\n<p><span style=\"color: black;\"><a style=\"color: #ff9900;\" href=\"https:\/\/thenextweb.com\/news\/why-most-machine-learning-models-never-hit-market-syndication\" target=\"_blank\" rel=\"noopener\">Source<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;#Why 90% of machine learning models never hit the market&#8221; Corporations are going through rough times. And I\u2019m not talking about the pandemic and the stock market volatility. The times are uncertain, and having to make customer experiences more and more seamless and immersive isn\u2019t taking off any of the pressure on companies. In that&#8230;<\/p>\n","protected":false},"author":1,"featured_media":302052,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/img-cdn.tnwcdn.com\/image\/neural?filter_last=1&fit=1280,640&url=https:\/\/cdn0.tnwcdn.com\/wp-content\/blogs.dir\/1\/files\/2021\/07\/broken-2591910_1920-broken-computer-machine-learning-failure.jpg&signature=9a47db418d2596359962a438a076f181","fifu_image_alt":"","footnotes":""},"categories":[18],"tags":[],"class_list":["post-302051","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"_links":{"self":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/302051","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/comments?post=302051"}],"version-history":[{"count":0,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/posts\/302051\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media\/302052"}],"wp:attachment":[{"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/media?parent=302051"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/categories?post=302051"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buradabiliyorum.com\/en\/wp-json\/wp\/v2\/tags?post=302051"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}