Why ModelOps is an enterprise-level capability under the CIO’s accountability
We all have heard of DevOps and the transformation it has brought to application delivery in enterprises. However, there is another equally powerful emerging capability, ModelOps, which can give the organization of the so-called Enterprise AI a competitive advantage too.
Today, artificial intelligence applications are widespread and perceptible even in everyday life. Consider, for example, the ability of a car to brake automatically or not to exit the lane during a curve or, also, to the health sector, where the AI is able to monitor and report anomalous trends as well as helping to streamline processes to free up doctors’ schedules and therefore reduce costs. Let’s think for example of a computer able to identify the famous person we have in mind, asking ourselves no more than 10 questions, or how AI is constantly being adopted by many organizations in the finance and business sectors to restructure companies, improve earnings and experiences, reduce risks, and increase opportunities for the financial engines of our modern economy.
These are already existing technologies of more or less advanced artificial intelligence, which are changing the way we live, work and study. When the machine learns independently, thanks to these and through experience, managing to improve its performance over time by providing more answers or more functions, you will have machine learning.
However, AI and machine learning models introduce new risks into model operationalization (post-development). Many model operations processes are manual or managed using home-grown solutions that constantly need to be updated as new technologies, tools and governance requirements are introduced.
For this reason, AI must move beyond ad-hoc, small-scale experimentation conducted by data scientists, to automated pipelines that can deliver inference results directly into enterprise environments.
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What is ModelOps?
Meanwhile, organizations, to remain competitive, are trying to develop AI and machine learning models at an increasing pace to gain new insights, continue their digital transformation and reimagine their business, as we have stated the risk of failure and wasting resources, still remains very high.
It is also well known that organizations have been using models of all kinds for decades to support business. However, AI and machine learning models introduce new risks related to the operationalization of the model itself. Such models, which are probabilistic and non-deterministic by default, need to be monitored and governed (sometimes retuned, retrained, or even replaced) to effectively support the development of AI initiatives at scale.
ModelOps, as a key strategic capability to deploy, monitor and govern model life cycle in production, across the entire enterprise AI, is a novel framework and platform for the end-to-end lifecycle management of artificial intelligence applications artefacts. To ensure scalability and governance, the most innovative enterprises are using ModelOps as an enabling technology, which is a crucial step in the convergence of various AI artefacts, platforms and solutions.
How does ModelOps fit with existing CIO organizations?
The best way to understand the role played by ModelOps in enterprise AI is to start by defining the components of ModelOps capability and determine how to organize and manage the effort. In order for enterprise AI to have an impact across the entire organization, the business must have a unified strategy that allows each part of the business, the flexibility to utilize the AI tools that are best suited to their needs while ensuring that the results of those tools flow to the business quickly, reliably, in full compliance, and with full accountability.
