ModelOps for Deploying AI

The term ModelOps refers to the governance and life cycle management of all artificial intelligence and decision models, including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents, according to Gartner. 

“In contrast to MLOps, which focuses only on the operationalization of ML models, and AIOps which is AI for IT Operations, ModelOps focuses on the operationalization of all AI and decision models,” Gartner says. 

source: ModelOp

As this video suggests, ModelOps aims to support scaling of artificial intelligence capabilities across an enterprise.

Some of you might see this as an example of an important business strategy, which is to create a new market. That is one way to avoid being “trapped” in battle aimed at taking market share from other competitors, and instead boosting value and uniqueness by competing in a new and different market. 

It is vendor push, to be sure, but such pushes--as opposed to buyer pulls-- fail if it does not align with an enterprise's need to solve a real problem important to its business model.

And Now, MLOps

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.