We do love our acronyms. Add MLOps--also known as ML CI/CD, ModelOps, and ML DevOps--to the lexicon. It’s perhaps like AIOps in the sense of applying artificial intelligence or machine learning to the information technology operations process.
“MLOps is an approach that marries and automates ML model development and operations, aiming to accelerate the entire model life cycle process,” says Deloitte. “MLOps features automated pipelines, processes, and tools that streamline all steps of model construction.”
As tends to happen when we develop new acronyms and concepts, we often see new firms emerging. Some might argue MLOps is more an engineering culture using DevOps principles than anything else.
“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,” Wikipedia says.
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, Wikipedia adds.
No comments:
Post a Comment