{"id":1579,"date":"2025-06-13T10:43:17","date_gmt":"2025-06-13T08:43:17","guid":{"rendered":"https:\/\/demand.nl\/?post_type=knowledgebase&p=1579"},"modified":"2025-06-26T12:28:52","modified_gmt":"2025-06-26T10:28:52","slug":"mlops_def","status":"publish","type":"knowledgebase","link":"https:\/\/demand.nl\/en\/wiki\/mlops\/mlops-general\/mlops_def\/","title":{"rendered":"MLOps"},"content":{"rendered":"

This page is the home of the DEMAND MLOps Workgroup. Here we will collect useful resources.<\/p>\n\n\n\n

Definition MLOps<\/h3>\n\n\n\n

The blogpost AI Engineering and MLOps: Building Production-Ready Machine Learning Systems<\/a> gives our working definition of MLOps (also called AI Engineering) as \u201can end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software\u201d. The blogpost Software Engineering for Machine Learning Applications<\/a> explains the difference with software engineering for rule-based software. In recent work we updated the process from that blogpost to the following picture, indicating that a data engineering stream is also needed.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

MLOps Tools<\/h3>\n\n\n\n

The blogpost A Toolbox for the Applied AI Engineer<\/a> summarizes a book chapter (available on request) about software engineering for machine learning applications. It contains a bulleted lists of useful tools (methods, techniques, libraries, frameworks, software tools, etc.) for AI engineering. The post Testing Machine Learning Applications<\/a> contains a more in-depth analysis of tools for testing ML applications.<\/p>\n\n\n\n

Data Engineering for MLOps<\/h3>\n\n\n\n

In the context of the DEMAND project we will focus more on the data-side of MLOps. This is a fairly new topic as can be seen in two recent papers:<\/p>\n\n\n\n

    \n
  1. What About the Data? A Mapping Study on Data Engineering for AI Systems<\/a><\/li>\n\n\n\n
  2. Garbage in, Garbage out. A Mapping Study on Data Quality Engineering for AI Systems<\/a><\/li>\n<\/ol>\n\n\n\n

    What looks like a promising avenue is to further work out the tooling for so-called DataOps<\/strong>, see the paper by Raj et al. (2020) From Ad-Hoc Data Analytics to DataOps<\/a>. See also https:\/\/lakefs.io\/blog\/the-state-of-data-engineering-2023\/<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

    This page is the home of the DEMAND MLOps Workgroup. Here we will collect useful resources. Definition MLOps The blogpost AI Engineering and MLOps: Building Production-Ready Machine Learning Systems gives our working definition of MLOps (also called AI Engineering) as \u201can end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable […]<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"open","ping_status":"open","template":"","knowledgebase_cat":[109],"class_list":["post-1579","knowledgebase","type-knowledgebase","status-publish","hentry","knowledgebase_cat-mlops-general"],"_links":{"self":[{"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/knowledgebase\/1579","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/knowledgebase"}],"about":[{"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/types\/knowledgebase"}],"author":[{"embeddable":true,"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/comments?post=1579"}],"version-history":[{"count":5,"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/knowledgebase\/1579\/revisions"}],"predecessor-version":[{"id":1598,"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/knowledgebase\/1579\/revisions\/1598"}],"wp:attachment":[{"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/media?parent=1579"}],"wp:term":[{"taxonomy":"knowledgebase_cat","embeddable":true,"href":"https:\/\/demand.nl\/en\/wp-json\/wp\/v2\/knowledgebase_cat?post=1579"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}