ModelOps Is Just The Beginning Of 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. We caught up following a roundtable that Stu moderated in September that featured many industry leaders along with Erick Brethenoux, VP Analyst with Gartner’s, and the lead for their AI research. Erick Brethenouxc introduced Gartner’s Enterprise AI framework, and the panelists highlighted key challenges that Enterprises are facing.
Enterprise AI spans different departments, and along with new AI initiatives, it often incorporates existing AI or Big Data implementations from the last decade. Enterprise AI presents a unique opportunity for CIOs to consolidate existing data warehouses, data analytics, and business intelligence applications from various departments to find company-wide use cases that will heavily impact the bottom line.
Each department and business unit will have its unique applications and use cases for AI and needs to freedom to use the most appropriate tools and techniques, which in the AI world are changing rapidly. But at a corporate level, there’s a need for unified approaches to governance and operations that necessitates a centralized, disciplined approach to modelops.
Stu Bailey,Co-founder and Chief Enterprise AI Architect of ModelOp says, “For almost all participants in our roundtable, everyone agreed that ModelOps lies at the center of the broader enterprise AI strategy.”
A recent Gartner webinar explains that successful organizations do two things that allow them to be more disciplined in their Enterprise AI approach.
Bailey says, “Every model represents a very unique piece of intellectual property derived from the company’s data. Models are created from data but they are quite different from datasets because they have complex relationships with the business structure.. As data change and as the business evolves models must change. They don’t fit into the patterns that have evolved to manage software because they encode the organization’s most valuable proprietary information.”
Enterprise AI projects touch many functional groups across the enterprise, including the data scientists who create models, the data teams that manage the data, the development and operations teams, the governance organization, and the business unit that sponsors the model’s development to meet a business KPI. These different groups tend to operate in silos, which creates friction and slows the process of moving models from the lab into production.
The role of ModelOps is to provide an efficient and transparent framework for operationalizing AI across the enterprise. It offers the opportunity to manage business accountability, share it across groups, and bring stakeholders such as compliance, finance, operations, and other business unit functions closer together to impact business outcomes.
Bailey says, “One thing the panel was very clear about is that ModelOps is more of a business accountability capability than a technical capability. It’s certainly is very technical, but it has to allow for model life cycles, integrate into existing compliance programs, and be a part of the risk management program. For example, there have to be audits, approvals, governance functions for models to be in production in the banking industry.
