{"id":2475,"date":"2021-08-03T22:53:23","date_gmt":"2021-08-04T06:53:23","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/blog\/what-are-the-challenges-and-shortcomings-of-machine-learning-today\/"},"modified":"2021-08-03T22:53:23","modified_gmt":"2021-08-04T06:53:23","slug":"what-are-the-challenges-and-shortcomings-of-machine-learning-today","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/what-are-the-challenges-and-shortcomings-of-machine-learning-today\/","title":{"rendered":"How to Tackle 3 Common Machine Learning Challenges"},"content":{"rendered":"\n
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https:\/\/vimeo.com\/582673299<\/div>\n<\/figure>\n\n\n\n


Here are 3 common machine learning challenges and how to tackle them:<\/p>\n\n\n\n

1. Building a good enough model:<\/p>\n\n\n\n

From our experience working with companies like Uber, Etsy, Zappos Family of Companies, Ancestry, and many more, typically, the biggest challenge in ML is building a model that’s good enough to provide business value.<\/p>\n\n\n\n

We often hear that 80% of ML models never make it to production.<\/p>\n\n\n\n

But I’m yet to meet a team with a good enough model but couldn’t figure out deployment.<\/p>\n\n\n\n

While deployment isn\u2019t trivial, it’s not as different from deploying an application.<\/p>\n\n\n\n

2. Identifying the business use case that is feasible from an ML perspective and can provide value:<\/p>\n\n\n\n

There needs to be an intersection between the business owners and data scientists within the organization.<\/p>\n\n\n\n

It typically means bringing both of these personas into the same room and having a conversation.<\/p>\n\n\n\n

That starts by correctly identifying the right business use case and making sure machine learning and data science are part of that process.<\/p>\n\n\n\n

3. Lack of predictability:

With machine learning and data science, we often don’t know if we’ll succeed beforehand. So instead of identifying one problem, we take a portfolio approach where ML teams explore multiple projects at once and only double down to extract signal from the data.<\/p>\n","protected":false},"excerpt":{"rendered":"

Here are 3 common machine learning challenges and how to tackle them: 1. Building a good enough model: From our experience working with companies like Uber, Etsy, Zappos Family of Companies, Ancestry, and many more, typically, the biggest challenge in ML is building a model that’s good enough to provide business value. We often hear […]<\/p>\n","protected":false},"author":1,"featured_media":2476,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[10],"tags":[],"coauthors":[107],"class_list":["post-2475","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry"],"yoast_head":"\nHow to Tackle 3 Common Machine Learning Challenges - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/what-are-the-challenges-and-shortcomings-of-machine-learning-today\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Tackle 3 Common Machine Learning Challenges\" \/>\n<meta property=\"og:description\" content=\"Here are 3 common machine learning challenges and how to tackle them: 1. Building a good enough model: From our experience working with companies like Uber, Etsy, Zappos Family of Companies, Ancestry, and many more, typically, the biggest challenge in ML is building a model that’s good enough to provide business value. We often hear […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/what-are-the-challenges-and-shortcomings-of-machine-learning-today\/\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2021-08-04T06:53:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/gideon-video-image.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"499\" \/>\n\t<meta property=\"og:image:height\" content=\"268\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Gideon Mendels\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Gideon Mendels\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"How to Tackle 3 Common Machine Learning Challenges - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/what-are-the-challenges-and-shortcomings-of-machine-learning-today\/","og_locale":"en_US","og_type":"article","og_title":"How to Tackle 3 Common Machine Learning Challenges","og_description":"Here are 3 common machine learning challenges and how to tackle them: 1. 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