Most of this year, enterprises have been reviewing the lessons learned in the past few years from their Enterprise AI initiatives, i.e., what has worked, what hasn't, and how to move forward to modernize their infrastructures and take full advantage of AI. According to Garner's recent research report, from 2018 to 2020, only around 47% of projects in enterprise organizations are in production. The rest are stuck in the pre-production phases. Many enterprises are still trying to get their AI projects into operation and contributing to the business. Last week, I spoke to Stu Bailey, the Co-founder and Chief Enterprise AI Architect at ModelOp, a company trying to help enterprises implement ModelOps, the key component in operationalizing enterprise AI.

Most of this year, enterprises have been reviewing the lessons learned in the past few years from their Enterprise AI initiatives, i.e., what has worked, what hasn't, and how to move forward to modernize their infrastructures and take full advantage of AI. According to Garner's recent research report, from 2018 to 2020, only around 47% of projects in enterprise organizations are in production. The rest are stuck in the pre-production phases. Many enterprises are still trying to get their AI projects into operation and contributing to the business. Last week, I spoke to Stu Bailey, the Co-founder and Chief Enterprise AI Architect at ModelOp, a company trying to help enterprises implement ModelOps, the key component in operationalizing enterprise AI.

In part one of this discussion, I presented the basic concept of model debt as a way to measure the effectiveness of individual models and AI programs overall. In part two, I'll go through a short example to show how model debt can be computed in practice. The target production days (TPDs), which is a count of the number of days that the model is intended to be in production over its full life cycle, starting from when the data science team releases it for production. The shorter the lock-to-load time, the faster the model can contribute to the business. The actual lock-to-load time will depend on how effectively the ModelOps process moves the model through its life cycle steps as defined by the enterprise AI architect, including technical checks (e.g., security scans, performance verification, etc.), governance requirements (e.g., regulatory compliance, explainability reports, etc.) and business considerations (e.g., agreement on KPIs, departmental sign-offs, etc.).

In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning models across the organization. At the center of this scaling up effort is ModelOp, the company that builds solutions to scale the processes that take models from the data science lab into production. Even before their recent $6 million Series A funding led by Valley Capital Partners with participation from Silicon Valley Data Capital, they are already the leader providing ModelOps solutions to Fortune 1000 companies. ModelOps is a capability that focuses on getting models into 24/7 production.