From 0a3765ec863d4050cce28325b7748219188669e6 Mon Sep 17 00:00:00 2001 From: Vicki Date: Mon, 22 Jun 2020 20:38:05 -0400 Subject: [PATCH] Fixing broken Wikipedia link --- index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/index.md b/index.md index 8820d8d..be4d123 100644 --- a/index.md +++ b/index.md @@ -6,7 +6,7 @@ layout: home From [Wikipedia](https://en.wikipedia.org/wiki/MLOps): -> MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle.[1] Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. +> MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) [lifecycle](https://www.aitrends.com/machine-learning/mlops-not-just-ml-business-new-competitive-frontier/). Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. ## How Can GitHub Help With MLOps?