{"id":9335,"date":"2020-08-14T09:15:37","date_gmt":"2020-08-14T09:15:37","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=9335"},"modified":"2023-11-20T16:29:37","modified_gmt":"2023-11-20T16:29:37","slug":"mlops-what-you-need-to-know","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/mlops-what-you-need-to-know\/","title":{"rendered":"MLOps: What You Need To Know"},"content":{"rendered":"\t\t
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MLOps is a relatively new concept in the AI (Artificial Intelligence) world and stands for \u201cmachine learning operations.\u201d\u00a0Its about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models.\u00a0<\/p>\n\n\n\n

\u201cMLOps is the natural progression of DevOps in the context of AI,\u201d said Samir Tout, who is a Professor of Cybersecurity at the\u00a0Eastern Michigan University’s School of Information Security & Applied Computing (SISAC)<\/a>.\u00a0\u201cWhile it leverages DevOps’ focus on security, compliance, and management of IT resources, MLOps\u2019 real emphasis is on the consistent and smooth development of models and their scalability.\u201d<\/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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The origins of MLOps goes back to 2015 from a paper entitled \u201cHidden Technical Debt in Machine Learning Systems.\u201d\u00a0And since then, the growth has been particularly strong.\u00a0Consider that the market for MLOps solutions is expected to reach $4 billion by 2025.\u00a0<\/p>\n\n\n\n

\u201cPutting ML models in production, operating models, and scaling use cases has been challenging for companies due to technology sprawl and siloing,\u201d said Santiago Giraldo, who is the Senior Product Marketing Manager and Data Engineer at\u00a0Cloudera<\/a>.\u00a0\u201cIn fact, 87% of projects don\u2019t get past the experiment phase and therefore, never make it into production.<\/p>\n\n\n\n

Then how can MLOps help?\u00a0Well, the handling of data is a big part of it.<\/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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\u201cSome key best practices are having a reproducible pipeline for data preparation and training, having a centralized experiment tracking system with well-defined metrics, and implementing a model management solution that makes it easy to compare alternative models across various metrics and roll back to an old model if there is a problem in production,\u201d said Matei Zaharia, who is the chief technologist at\u00a0Databricks<\/a>.\u00a0\u201cThese tools make it easy for ML teams to understand the performance of new models and catch and repair errors in production.\u201d<\/p>\n\n\n\n

Something else to consider is that AI models are subject to change.\u00a0This has certainly been apparent with the COVID-19 pandemic.\u00a0The result is that many AI models have essentially gone haywire because of the lack of relevant datasets.\u00a0<\/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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\u201cPeople often think a given model can be deployed and continue operating forever, but this is not accurate,\u201d said Randy LeBlanc, who is the VP of Customer Success at\u00a0RapidMiner<\/a>.\u00a0\u201cLike a machine, models must be continuously monitored and maintained over time to see how they\u2019re performing and shifting with new data\u2013ensuring that they\u2019re delivering real, ongoing business impact.\u00a0MLOps also allows for faster intervention when models degrade, meaning greater data security and accuracy, and allows businesses to develop and deploy models at a faster rate. For example, if you discovered an algorithm that will save you a million dollars per month, every month this model isn\u2019t in production or deployment costs you $1 million.\u201d<\/p>\n\n\n\n

MLOps also requires rigorous tracking that is based on tangible metrics.\u00a0If not, a project can easily go off the rails.\u00a0\u201cWhen monitoring models, you want to have standard performance KPIs as well as those that are specific to the business problem,\u201d said Sarah Gates, who is an Analytics Strategist at\u00a0SAS<\/a>.\u00a0\u201cThis should be through a central location regardless of where the model is deployed or what language it was written in.\u00a0That tracking should be automated\u2013so you immediately know and are alerted\u2014when performance degrades.\u00a0Performance monitoring should be multifaceted, so you are looking at your models from different perspectives.\u201d<\/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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While MLOps tools can be a huge help, there still needs to be discipline within the organization.\u00a0Success is more than just about technology.\u00a0<\/p>\n\n\n\n

“Monitoring\/testing of models requires a clear understanding of the data biases,” said Michael Berthold, who is the CEO and co-founder of\u00a0KNIME<\/a>. “Scientific research on event, model change, and drift detection has most of the answers, but they are generally ignored in real life. You need to test on independent data, use challenger models and have frequent recalibration. Most data science toolboxes today totally ignore this aspect and have a very limited view on ‘end-to-end’ data science.”<\/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":"

MLOps is a relatively new concept in the Artificial Intelligence world and stands for \u201cmachine learning operations.\u201d It\u2019s about how to best manage data scientists and operations people to allow for the effective development, deployment and monitoring of models. <\/p>\n","protected":false},"author":667,"featured_media":9336,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[226,128,225,549],"ppma_author":[3440],"class_list":["post-9335","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-ai","tag-devops","tag-ml","tag-mlops"],"authors":[{"term_id":3440,"user_id":667,"is_guest":0,"slug":"tom-taulli","display_name":"Tom Taulli","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_63f5f357-f6ae-4999-b3f4-ed30406eb86c-150x150.jpg","user_url":"http:\/\/www.taulli.com\/","last_name":"Taulli","first_name":"Tom","job_title":"","description":"Tom Taulli is an author, speaker, and advisor \u2013 with a focus primarily on technology. He has co-founded a variety of\u00a0companies, including Hypermart.net (sold to InfoSpace), WebIPO and BizEquity. He is also a contributor to Forbes.com, Bloomberg.com, Kiplinger and BusinessWeek. \u00a0He has also written a variety of books, mostly on tech and finance.\u00a0 His latest is\u00a0Artificial Intelligence Basics: A Non-Technical Introduction<\/a>. \u00a0As of now, he is an advisor to some awesome startups."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9335","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\/667"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=9335"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9335\/revisions"}],"predecessor-version":[{"id":34191,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/9335\/revisions\/34191"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/9336"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=9335"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=9335"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=9335"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=9335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}