{"id":9275,"date":"2020-08-10T07:51:18","date_gmt":"2020-08-10T07:51:18","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=9275"},"modified":"2023-11-21T13:39:03","modified_gmt":"2023-11-21T13:39:03","slug":"why-businesses-fail-at-machine-learning","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/why-businesses-fail-at-machine-learning\/","title":{"rendered":"Why Businesses Fail At Machine Learning"},"content":{"rendered":"\t\t
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I\u2019d like to let you in on a secret: when people say \u2018machine learning<\/a>\u2019 it sounds like there\u2019s only one discipline here. There are two, and if businesses don\u2019t understand the difference, they can experience a world of trouble.<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A tale of two machine learnings<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Imagine hiring a chef to build you an oven or an electrical engineer to bake bread for you. When it comes to machine learning, that\u2019s the kind of mistake I see businesses making over and over.<\/p>\n\n\n\n

If you\u2019re opening a bakery, it\u2019s a great idea to hire an experienced baker well-versed in the nuances of making delicious bread and pastry. You\u2019d also want an oven. While it\u2019s a critical tool, I bet you wouldn\u2019t charge your top pastry chef with the task of knowing how to build that oven; so why is your company focused on the equivalent for machine learning?<\/p>\n\n\n\n

Are you in the business of making bread? Or making ovens?<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Machine learning\u00a0research<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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What they don\u2019t tell you is that all those machine learning courses and textbooks are about how to build ovens (and microwaves, blenders, toasters, kettles\u2026 the kitchen sink!) from scratch, not how to cook things and innovate with recipes.<\/p>\n\n\n\n

If you build machine learning algorithms, your focus is general purpose tools for others to use. (Kitchen appliances, if you prefer the analogy.) This business is called\u00a0machine learning research<\/strong>\u00a0and is typically done by places like\u00a0academia<\/a>\u00a0or\u00a0Google<\/a>.<\/p>\n\n\n\n

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When it comes to machine learning, many organizations are in the wrong business.<\/p>\n<\/blockquote>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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You need quite a lot of education to be in this line of work, because there\u2019s a long history here. Some popular\u00a0algorithms<\/a>\u00a0have been around for centuries. For example, the method of least squares for regression, was\u00a0published in 1805<\/a>. Trust me, humanity has come a long way in 200 years.<\/p>\n\n\n\n

Today, there are some pretty sophisticated appliances out there\u2026 how are you going to build a better microwave if you don\u2019t know how this one works? Of course you need all that immersive study! Becoming a researcher takes years and there\u2019s a good reason that the 101 course starts with the basics of calculus.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Applied machine\u00a0learning<\/h2>\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Most businesses just want to get cooking\u200a\u2014\u200ato solve their business problems. They have no interest in selling microwaves, and yet often make the mistake of trying to build those appliances from scratch. It\u2019s hard to blame them\u200a\u2014\u200athe current hype and education cycle dominantly focuses on research, instead of application.<\/p>\n\n\n\n

If you\u2019re innovating with recipes, don\u2019t reinvent the wheel. Those microwaves exist already. You can get them for free from\u00a0many places<\/a>. And if setting up your own machine learning kitchen sounds like a chore, providers like\u00a0Google Cloud Platform<\/a>\u00a0let you use theirs, complete with\u00a0appliances<\/a>,\u00a0ingredients<\/a>, and\u00a0recipe books<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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If you\u2019re innovating in the kitchen, don\u2019t reinvent the\u00a0wheel.<\/p>\n<\/blockquote>\n\n\n\n

For most\u00a0applications<\/a>, your team doesn\u2019t need to understand the\u00a0mathematics of backpropagation in neural networks<\/a>\u00a0any more than a chef needs to know the wiring diagram for a microwave. But there\u2019s a lot that you do need to know if you\u2019re planning on running an industrial-scale kitchen, everything from curating your ingredients to checking that your dishes are good before you serve them.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Crashing and burning with machine\u00a0learning<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Unfortunately, I see a lot of businesses failing to get value from machine learning because they don\u2019t realize that the applied side is a very different discipline from the algorithms research side. Instead,\u00a0leaders try to start their kitchens by hiring those folks who\u2019ve been building microwave parts their whole lives but have never cooked a thing.<\/strong>\u00a0What could possibly go wrong? If that works out, it\u2019s because you got lucky and accidentally hired an engineer who is a great chef.<\/p>\n\n\n\n

But usually you\u2019re not lucky. There are only so many hours in one lifetime, and if you spend them learning how a microwave is wired, you\u2019ve got fewer to devote to mastering the art of pastry or business. Where\u200a\u2014\u200aand when!\u200a\u2014\u200awould your PhD-trained artificial intelligence researcher have gained the skills required for applied machine learning<\/a>? If you set your heart on the hybrid who\u2019s an expert in both, no wonder you\u2019re complaining about the talent shortage!<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Whom should you hire instead? Just like in an industrial kitchen, you need an interdisciplinary team with leadership that understands this space. Otherwise, projects fizzle and go nowhere.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Hiring the right team for the\u00a0job<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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If you\u2019re selling cutting-edge appliances, hire researchers. If you\u2019re innovating in recipes to sell food at scale, you need people who figure out what\u2019s worth cooking \/ what the objectives are (decision-makers and product managers<\/em>), people who understand the suppliers and the customers (domain experts and social scientists<\/em>), people who can process ingredients at scale (data engineers and analysts<\/em>), people who can try many different ingredient-appliance combinations quickly to generate potential recipes (applied ML engineers<\/em>), people who can check that the quality of the recipe is good enough to serve (statisticians<\/em><\/a>), people who turn a potential recipe into millions of dishes served efficiently (software engineers<\/em>), people who keep the interdisciplinary team on track (project\/program managers<\/em>), and people who ensure that your dishes stay top notch even if the delivery truck brings you a ton of potatoes instead of the rice you ordered (reliability engineers<\/em>).<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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While these needn\u2019t be separate individuals, be sure you\u2019ve got each role covered. And before you fling your rotten tomato at me for providing such an incomplete caricature, I\u2019ll freely admit that there\u2019s much more to say about hiring for applied machine learning. I\u2019ve outsourced that to other posts, including\u00a0this one<\/a>.<\/p>\n\n\n\n

Speaking of outsourcing, if your team has tried all existing tools and can\u2019t make a recipe that meets your business objectives, it makes sense to think about adding skills in building appliances (researcher<\/em>). Whether or not you hire that person to your permanent staff or outsource the job to an experienced algorithms research firm depends on the scale and maturity of your operation.<\/p>\n\n\n\n

Another reason to connect with researchers is that your prototype is so successful that using custom-built appliances makes sense at the massive scale you\u2019re lucky enough to operate at. (What a great problem to have!)<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Decision intelligence<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Experts should be talking about this, but they aren\u2019t. They\u2019re not owning up to the fact that there\u2019s really two machine learnings here, and so the world is training people in building all these algorithms but not in using them.<\/p>\n\n\n\n

My team is working to fix that. We\u2019ve created a new discipline to cover the applied side and we\u2019ve already trained over 15,000 staff members in it. We\u2019re calling it\u00a0decision intelligence engineering<\/strong>, and it spans all the applied aspects of machine learning and data science.<\/p>\n\n\n\n

To put it another way, if research machine learning is building microwaves and applied machine learning is using microwaves,\u00a0decision intelligence engineering<\/a>\u00a0is using microwaves safely to meet your goals and using something else when you don\u2019t need a microwave.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Good luck and have\u00a0fun!<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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When it comes to applied machine learning, the hardest part is knowing what you want to cook and how you plan to check it before you serve it to your customers. That part is actually not that hard\u200a\u2014\u200ajust don\u2019t forget to do it<\/a>.<\/p>\n\n\n\n

As for the rest, solving business problems with machine learning is far easier than most people think. Those gleaming kitchens are waiting for you to come play in them. Dive in as you would in a real kitchen. Start tinkering! Every time I meet someone who believes they need to take a traditional machine learning algorithms course\u200a\u2014\u200aor, goodness! a whole degree\u200a\u2014\u200ain order to get started, I can\u2019t help but imagine them refusing to use microwaves until they built one themselves. Don\u2019t fall for the lie that says you need a PhD to do amazing things with machine learning. Instead, what you really need is a bit of\u00a0human creativity<\/a>. Good luck and have fun!<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

When people say \u2018machine learning\u2019 it sounds like there\u2019s only one discipline here. There are two, and if businesses don\u2019t understand the difference, they can experience a world of trouble<\/p>\n","protected":false},"author":335,"featured_media":9276,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[390,92,225],"ppma_author":[2050],"class_list":["post-9275","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-businesses","tag-machine-learning","tag-ml"],"authors":[{"term_id":2050,"user_id":335,"is_guest":0,"slug":"cassie-kozyrkov","display_name":"Cassie Kozyrkov","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_df35f80d-2bff-4fe3-b741-a94d51320e00-150x150.jpg","user_url":"https:\/\/careers.google.com\/?src=Online\/LinkedIn\/linkedin_profilepage&utm_source","last_name":"Kozyrkov","first_name":"Cassie","job_title":"","description":"Cassie Kozyrkov is Chief Decision Scientist at Google, Inc. With a unique combination of deep technical expertise, and world-class public-speaking skills, she has provided guidance on more than 100 projects and designed Google's analytics program, personally training over 15000 Googlers in statistics, decision-making, and machine learning.\u00a0"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9275","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/335"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=9275"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9275\/revisions"}],"predecessor-version":[{"id":34237,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9275\/revisions\/34237"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/9276"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=9275"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=9275"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=9275"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=9275"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}