{"id":11206,"date":"2022-07-06T10:06:47","date_gmt":"2022-07-06T09:06:47","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=11206"},"modified":"2024-12-10T02:23:13","modified_gmt":"2024-12-10T02:23:13","slug":"mlops-devops-team-skills-roles-explained","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/mlops-devops-team-skills-roles-explained\/","title":{"rendered":"Planning for Success: Suggested MLOps Team Structure"},"content":{"rendered":"<p>Data scientists play a vital role in operationalising machine learning. Within MLOps, data scientists are responsible for developing, evaluating and amending models as they move into production.<\/p>\n<p>But what happens when it comes to software or platform knowledge, and integrating models into business systems?<\/p>\n<p><iframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/PPfmC0ot704\" width=\"560\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p>A successful MLOps team absolutely needs data scientists \u2013 but that isn\u2019t all. If you want to deploy ML models successfully, you\u2019ll need\u00a0 a cross-functional team of experts who provide a number of important skills. An ideal MLOps team structure should include:<\/p>\n<ul>\n<li>Data scientists to create, build and amend the model<\/li>\n<li>Platform or machine learning engineers to provide an environment to host the model<\/li>\n<li>Data engineers to create the production data pipelines to retrain the model<\/li>\n<li>Software engineers to integrate the model into business systems<\/li>\n<\/ul>\n<p>It\u2019s worth noting that in some smaller organisations, these roles might be part-time and performed by one person, or one person might fulfil more than one role. In larger organisations, there could be multiple people providing each function.<\/p>\n<p>No matter the size of your team, it\u2019s important that everyone has an idea of the responsibilities and requirements of other team members and roles. As your model moves from prototype to production, it\u2019s important everyone understands the concerns of other team members, and the format and type of information that needs to be provided.<\/p>\n<p>Building a cross-functional team means that your MLOps development benefits from a broader skillset. The more your team members understand the skills of the wider team, the more effectively they can work together.<\/p>\n<ul>\n<li>Engineers should recognise that the most pressing concern for data scientists is prototyping, experimentation and algorithm performance evaluation.<\/li>\n<li>Data scientists often need to learn more about software development practices, and the separation of environments such as development, staging and production.<\/li>\n<\/ul>\n<p>Ultimately, the goal of a cross-functional team is to create a clear framework that takes models through the entire development and production process. This framework should be built into the CI\/CD framework. Create a simple document and spend a session taking data scientists through the development process that you have chosen. When the team forms, recognise that it is one team and organise yourself accordingly. Backlogs and stand-ups should be owned by and include the whole team.<\/p>\n<p>If you\u2019d like to know more about building effective MLOps team structures and operationalising machine learning, download our <a href=\"https:\/\/www.equalexperts.com\/our-services\/data\/mlops\/\">recent playbook<\/a>, which is packed with insights into building successful MLOps projects and getting them into production.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data scientists are critical to MLOps, but they can\u2019t do the whole job of productionising ML models. EE explains how to build a cross-functional team to drive successful projects.<\/p>\n","protected":false},"author":133,"featured_media":11208,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[266,267,265],"location":[397],"class_list":["post-11206","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-focus","tag-devops","tag-machine-learning","tag-mlops"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Planning for Success: Suggested MLOps Team Structure | Equal Experts<\/title>\n<meta name=\"description\" content=\"Deploying ML models successfully is a team affair. Find out what key skills and roles you need as part of your cross-functional MLOps team.\" \/>\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.equalexperts.com\/blog\/tech-focus\/mlops-devops-team-skills-roles-explained\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Planning for Success: Suggested MLOps Team Structure\" \/>\n<meta property=\"og:description\" content=\"Data scientists are critical to MLOps, but they can\u2019t do the whole job of productionising ML models. EE explains how to build a cross-functional team to drive successful projects.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.equalexperts.com\/blog\/tech-focus\/mlops-devops-team-skills-roles-explained\/\" \/>\n<meta property=\"og:site_name\" content=\"Equal Experts\" \/>\n<meta property=\"article:published_time\" content=\"2022-07-06T09:06:47+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-10T02:23:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2022\/07\/MLOps-teamwork-fb-.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1201\" \/>\n\t<meta property=\"og:image:height\" content=\"631\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Simon Case\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Planning for Success: Suggested MLOps Team Structure\" \/>\n<meta name=\"twitter:description\" content=\"Data scientists are critical to MLOps, but they can\u2019t do the whole job of productionising ML models. 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