{"id":1079,"date":"2020-05-06T09:10:36","date_gmt":"2020-05-06T09:10:36","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=1079"},"modified":"2020-05-06T09:10:41","modified_gmt":"2020-05-06T09:10:41","slug":"whats-with-all-the-ops","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2020\/05\/06\/whats-with-all-the-ops\/","title":{"rendered":"What\u2019s with all the Ops?"},"content":{"rendered":"\n
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Photo by h heyerlein on Unsplash\n<\/figcaption><\/figure>\n\n\n\n

DevOps, DataOps, MLOps \u2013 the number of different \u201cOps\u201d combinations seems to have exploded over the last year or so. There are manifestos, meetups, lots of blog posts and research articles about these various approaches.<\/p>\n\n\n\n

In order to get clear on terminology, I think it\u2019s good to define \nwhat we\u2019re talking about. So, first, DevOps is a set of practices that \ncombines software development (Dev) and information technology \noperations (Ops) with the aim to shorten the system development life \ncycle and provide continuous delivery with high software quality \n(Wikipedia). The intent is to combine agile software development \npractices with continuous deployment in order to have a constant flow of\n new functionality and resultant value delivery to customers. Also \nreferred to as continuous deployment, new functionality can be rolled \nout whenever it\u2019s ready, the effects measured and the feedback used to \ninform the next (rapid) cycle of development.<\/p>\n\n\n\n

DataOps is an automated, process-oriented methodology, used by \nanalytic and data teams, to improve the quality and reduce the cycle \ntime of data analytics (Wikipedia). Although this sounds very different \nfrom DevOps, in most product companies, it\u2019s tightly interconnected with\n the products deployed in the field. Consequently, the data analytics is\n primarily focused on R&D teams that need to know if the intended \noutcomes of their development efforts are indeed accomplished as part of\n the continuous deployment pipeline.<\/p>\n\n\n\n

Finally, MLOps is a practice for collaboration and communication \nbetween data scientists and operations professionals to help manage the \nproduction machine learning (or deep learning) life cycle (Wikipedia). \nWhereas traditionally, data scientists would develop a model based on a \ndata set and then move on with their lives, currently in many systems, \nML\/DL models are constantly evolving due to changes to the data or new \nalgorithmic insights and need to be deployed frequently as well. Once \ndeployed, they need to be monitored to ensure that models that perform \nbetter in training also perform better during operations.<\/p>\n\n\n\n

In an earlier article<\/a>, I presented the HoliDev model (see figure). Each of the \u201cOps\u201d matches with one of the three types of development that\u2019s ongoing. The surprising thing, of course, is that \u201cOps\u201d for all these stands for \u201coperations\u201d and the key is to remember that for any system, product or solution, there\u2019s only one operations function taking care of it. So, Dev, Data and ML all have to integrate with the same Ops.<\/p>\n\n\n\n

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The HoliDev model<\/figcaption><\/figure>\n\n\n\n

Concluding, whatever \u201cOps\u201d you\u2019re working on, it all has to come together in the same operations and consequently, you\u2019ll need to work in cross-functional teams<\/a> to ensure that you\u2019re reaching the desired outcomes. The important takeaway is, though, that if an activity delivers value to customers, it deserves being done often. Only unimportant tasks are done yearly or even less frequently. So, reflect on this: for all the value-adding activities and processes in your organization, how can you increase the cycle time and create your own \u201cOps\u201d setup where it matters the most?<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at <\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

DevOps, DataOps, MLOps \u2013 the number of different \u201cOps\u201d combinations seems to have exploded over the last year or so. There are manifestos, meetups, lots of blog posts and research articles about these various approaches. In order to get clear on terminology, I think it\u2019s good to define what we\u2019re talking about. So, first, DevOps … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[15,4,3,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1079"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1079"}],"version-history":[{"count":1,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1079\/revisions"}],"predecessor-version":[{"id":1081,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1079\/revisions\/1081"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1079"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1079"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1079"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}