{"id":10968,"date":"2022-05-05T08:53:12","date_gmt":"2022-05-05T07:53:12","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=10968"},"modified":"2023-09-22T00:20:29","modified_gmt":"2023-09-21T23:20:29","slug":"what-is-mlops","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/what-is-mlops\/","title":{"rendered":"What is MLOps?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Building a predictive model to forecast the future from historical data is standard practice for today\u2019s businesses. But deploying, scaling and managing these models is far from simple. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Each ML solution depends on an algorithm (code) and a set of data used to develop and train the algorithm. For this reason, building ML solutions is different to other types of software development.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Enter MLOps, or machine learning operations, a set of processes that help organisations to develop, deploy and monitor ML models at scale by applying best practices to infrastructure, code and data.\u00a0<\/span><\/p>\n<p><iframe title=\"What is MLOPs\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/hV0kCg58jLI?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><span style=\"font-weight: 400;\">MLOps is a relatively new idea but one that has been adopted by many organisations &#8211; the market for MLOps solutions is expected to reach $4 billion by 2025. At Equal Experts, we have been involved in developing and deploying AI and ML for a number of applications including to:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assess cyber risk\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Evaluate financial risk\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Improve search recommendations for retail websites\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Improve logistics and supply chains\u00a0<\/span><\/li>\n<\/ul>\n<h2><b>Key Terms used in MLOps<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">If you\u2019re new to MLOps there are several important terms to be aware of:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Machine learning (ML)<\/b><span style=\"font-weight: 400;\"> &#8211; a subset of AI that involves training algorithms with data rather than developing hand-crafted algorithms. A machine learning solution uses\u00a0 a data set to train an algorithm, typically training a classifier that says what type of thing\u00a0 this data is (e.g. this picture is of a dog ); a regressor, which estimates\u00a0 a value (e.g. the price of this house is \u00a3400,000.) or an unsupervised\u00a0 model, such as generative ones\u00a0 which can be used to write novel text (such as song lyrics).\u00a0\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Model<\/b><span style=\"font-weight: 400;\"> &#8211; In machine learning a model is the result of training an algorithm with data, which maps a defined set of\u00a0 inputs to outputs.\u00a0\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Algorithm<\/b><span style=\"font-weight: 400;\"> &#8211; we use this term more or less interchangeably with model. (There are some subtle differences, but they\u2019re not important and using the term \u2018algorithm\u2019 prevents confusion with the standard software engineering use of the term \u2018data model\u2019 &#8211; which is a definition of the data entities, fields, relationships etc\u00a0 for a given domain, that is used to define database structures among other things.)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ground-truth data<\/b><span style=\"font-weight: 400;\"> &#8211; a machine-learning solution usually needs a data set that contains the input data (e.g. pictures) along with the associated answers (e.g. this picture is of a dog, this one is of a cat)\u00a0 &#8211; this is the\u00a0 \u2018ground-truth\u2019.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Labelled data<\/b><span style=\"font-weight: 400;\"> &#8211; means the same as ground-truth data.\u00a0<\/span><\/li>\n<\/ul>\n<h2><b>How does MLOps work?\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">We talk about MLOps as a set of processes that help data scientists to develop consistent, scalable ML models, and monitor their performance. To create and use these algorithms, you will usually follow these steps:\u00a0<\/span><\/p>\n<p><b>Initial development of the algorithm<\/b><span style=\"font-weight: 400;\"> &#8211; Developing a model is the first step in machine learning. Data scientists will identify or create \u2018ground truth\u2019 data sets and explore them. They will build and evaluate prototypes of the models, trying out different core algorithms and data transformations\u00a0 until they arrive at\u00a0 one which meets the business need.<\/span><\/p>\n<p><b>Integrate\/deploy the model<\/b><span style=\"font-weight: 400;\"> &#8211; once the model has been built, it must be integrated into the business. This can be done in various ways depending on the consuming service. In modern architecture, models are commonly implemented as a standalone microservice and models are deployed by copying an approved version of the model into an operational environment.\u00a0<\/span><\/p>\n<p><b>Monitor performance<\/b><span style=\"font-weight: 400;\"> &#8211; All ML models need to be monitored to ensure they\u2019re running and meeting demand, but also that the results of the model are accurate and reliable.<\/span><\/p>\n<p><b>Update model<\/b><span style=\"font-weight: 400;\"> &#8211; over time, models must be retrained to reflect new data, or improvements to the model. In this case, it\u2019s important to maintain version control and to direct downstream services to the new model.\u00a0\u00a0<\/span><\/p>\n<h2><b>Operationalising Machine Learning\u00a0<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Our<\/span><a href=\"https:\/\/www.equalexperts.com\/our-services\/data\/mlops\/\"><span style=\"font-weight: 400;\"> MLOps playbook<\/span><\/a><span style=\"font-weight: 400;\">\u00a0brings together our experiences working with algorithm developers to build ML solutions. It provides a comprehensive overview of what you need to consider when providing the architecture, tools and infrastructure to support data scientists and to integrate their outputs into the business.<\/span><\/p>\n<p><a href=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2022\/05\/MLOPS_Playbook_v3.1.pdf\"><span style=\"font-weight: 400;\">Download the playbook<\/span><\/a><span style=\"font-weight: 400;\"> for expert guidance on how your organisation can attain\u00a0 the promised business value from algorithms by providing engineering to support algorithm development, and by integrating ML more effectively into your business processes. You\u2019ll find helpful advice on how to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Collect data that drives machine learning, and make that available to data scientists\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integrate algorithms into your everyday business\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Configuration control, deploy and monitor deployed algorithms\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Test and monitor the algorithms\u00a0\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">View our <a href=\"https:\/\/www.equalexperts.com\/our-services\/data\/mlops\/\">online version<\/a> or download a pdf <a href=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2022\/05\/MLOPS_Playbook_v3.1.pdf\">here<\/a>.\u00a0\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our introductory guide explains how adopting MLOps can help organisations to develop, deploy and manage machine learning (ML) models at scale, helping solve the problem of AI projects that don\u2019t get off the starting blocks. <\/p>\n","protected":false},"author":133,"featured_media":0,"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":[99,266,267,265],"location":[397],"class_list":["post-10968","post","type-post","status-publish","format-standard","hentry","category-tech-focus","tag-ai","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>What is MLOps? 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