[{"data":1,"prerenderedAt":721},["ShallowReactive",2],{"/en-us/blog/the-road-to-smarter-code-reviewer-recommendations/":3,"navigation-en-us":37,"banner-en-us":465,"footer-en-us":482,"Taylor McCaslin":692,"next-steps-en-us":706},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"content":16,"config":27,"_id":30,"_type":31,"title":32,"_source":33,"_file":34,"_stem":35,"_extension":36},"/en-us/blog/the-road-to-smarter-code-reviewer-recommendations","blog",false,"",{"title":9,"description":10,"ogTitle":9,"ogDescription":10,"noIndex":6,"ogImage":11,"ogUrl":12,"ogSiteName":13,"ogType":14,"canonicalUrls":12,"schema":15},"The road to smarter code reviewer recommendations","Machine learning is coming to GitLab's code review process. Here's what you need to know, and how you can help!","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749668426/Blog/Hero%20Images/retrospectivesgitlabpost.jpg","https://about.gitlab.com/blog/the-road-to-smarter-code-reviewer-recommendations","https://about.gitlab.com","article","\n                        {\n        \"@context\": \"https://schema.org\",\n        \"@type\": \"Article\",\n        \"headline\": \"The road to smarter code reviewer recommendations\",\n        \"author\": [{\"@type\":\"Person\",\"name\":\"Taylor McCaslin\"}],\n        \"datePublished\": \"2022-01-04\",\n      }",{"title":9,"description":10,"authors":17,"heroImage":11,"date":19,"body":20,"category":21,"tags":22},[18],"Taylor McCaslin","2022-01-04","\nYou may recall back in June 2021, we [announced the acquisition of UnReview](/press/releases/2021-06-02-gitlab-acquires-unreview-machine-learning-capabilities/), a machine learning (ML) based solution for automatically identifying appropriate expert [code reviewers](/stages-devops-lifecycle/create/) and controlling review workloads and distribution of knowledge.\n\nAt the start of the new year we wanted to provide an update on our integration progress and our wider vision of leveraging machine learning to make GitLab's [DevOps Platform](/solutions/devops-platform/) smarter. You see, the acquisition of UnReview also was the initial staffing of [our new ModelOps stage](/direction/modelops/).\n\n### Our Newest DevOps Stage\n\nThis new stage, which we’ve named ModelOps, is focused on enabling and empowering data science workloads on GitLab. GitLab ModelOps aims to bring data science into GitLab both within existing features to make them smarter and more intelligent, but also empowering GitLab customers to build and integrate data science workloads within GitLab.\n\nSo what is ModelOps you may wonder? We view ModelOps as an all encompassing term to cover the entire end to end lifecycle of artificial intelligence models. We wanted to set our vision wide to fully cover everything needed to power data science workloads. DataOps is the processing of data workloads (think traditional ELT: extract, load, transform) and MLOps is the building, training, and deployment of machine learning models. If you’re confused don’t worry, it’s a lot to wrap your head around.\n\n![a look at the stages of MLOps](https://about.gitlab.com/images/blogimages/MLops.png){: .shadow.small.center}\n\nToday our DevOps Platform helps plan, build, test, secure, deploy, and monitor traditional software. Now we want to extend our DevOps Platform to include AI and ML workloads. If this is interesting to you, be sure to check out our recent Contribute talk where we dive deeper into plans for our ModelOps stage.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/C08QVI99JLo\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n### UnReview as our first feature\n\nSo what does this have to do with UnReview? 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