AI is fast becoming critical to business and IT applications and operations. Organizations have been investing in artificial intelligence capabilities for years to stay competitive, are hiring the best data scientist teams and are investing more and more in artificial intelligence and machine learning systems. However, implementing AI / ML models is not easy and the risk of failure is just around the corner. A solid methodology is needed to reduce this risk and enable companies to succeed. AI executives have been working toget more models in business for years now.

Machines play a huge role in our lives, including the machines we use every day, but without maintenance, every machine will eventually break down. Companies follow various maintenance programs to increase operational reliability and reduce costs. Maintenance is the set of operations necessary to preserve the functionality and efficiency of an asset and can take place in response to a failure or as a previously planned action. According to research conducted by Deloitte, a non-optimized maintenance strategy can reduce the production capacity of an industrial plant by 5 to 20%. Recent studies also show that downtime costs industrial manufacturers about 45 billion euros a year.

Organizations have been investing in artificial intelligence capabilities for years to stay competitive, are hiring the best data scientist teams and are investing more and more in artificial intelligence and machine learning systems. However, implementing AI / ML models is not easy and the risk of failure is just around the corner. A solid methodology is needed to reduce this risk and enable companies to succeed. AI executives have been working to get more models in business for years now. The first hurdle was getting data scientists hired and tools for rapid model creation.

These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them. Did you know approximately half of the AI models that are developed never actually make it into production? If you want to understand why and prevent the waste of data scientist time and other resources from happening at your organization, then it is important to understand the difference between MLOps and ModelOps. They aren't the same, but the terms are often used interchangeably.

AI executives have been working to get more models in business for years now. The first hurdle was getting data scientists hired and tools for rapid model creation. That problem has been solved. The next hurdle is getting those models into production in a timely, compliant manner. Companies have a backlog of models that are sitting idle and degrading -- contributing no value/revenue to the business.

One area marked by confusion today is understanding the differences between ModelOps vs. MLOps. ModelOps is the missing link for today's approach, connecting together existing data management solutions and model training tools to the value delivered via business applications. By incorporating ModelOps into your AI pipeline, you'll move past last-mile challenges with operationalizing AI and begin to see the return on your investments in the form of reduced costs, increased revenues, and better risk management. Recently, ModelOps has emerged as the critical link to addressing last-mile delivery challenges for AI deployments. ModelOps is a superset of MLOps, which refers to the processes involved to operationalize and manage AI models in use in production systems.