[Editor note] Schedule for Wed, July 21st Decisions organizations make today about building their AI pipelines will have major impacts on the future of AI maintenance for their organizations. But is everyone thinking about the future? The path taken today could impact the ability to add new functionality in the future or require starting from scratch. In order to build the right AI pipeline for your organization, you must identify the risk mix of tools to address the different parts of the pipeline, avoid vendor lock, and control costs. 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. ModelOps – an extension of MLOps 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. ModelOps tools provide all the capabilities of MLOps, but also provide two important additions: ModelOps tools allow you to operationalize all AI models, whereas MLOps tools focus primarily on machine learning models. While MLOps tools allow collaboration amongst various teams and stakeholders involved in building AI-enabled applications (data science teams, machine learning engineers, software developers), ModelOps tools provide dashboards, reporting, and information for business leaders. This provides teams with transparency and autonomy to work in a collaborative manner for AI at scale. Figure 1 – ModelOps vs. MLOps Because all information is governed, tracked, and auditable, ModelOps tools provide transparency into AI usage across an enterprise. Not only is this essential for monitoring model performance, drift detection, and retraining for AI models, but it enables insight into AI health. Teams can better manage and plan for infrastructure costs, while also maintaining control over access to sensitive business data through governance and role-based access control. By automating the logging and tracking of this information, data science teams, machine learning engineers, and software development teams can focus on building and maintaining systems, while business and IT leaders can easily access reporting metrics for ongoing monitoring. ModelOps Matters ModelOps will be one key to unlocking value with AI for the enterprise. If you look at all the other parts of the AI pipeline – data management, data wrangling, model training, model deployment and management, and business applications, ModelOps is the connective tissue. It links the disparate pieces of the pipeline to deliver value through business applications. By providing a shared tool to track and manage AI assets across all management stakeholders, an organization can: Reduce risks associated with “shadow” solutions built outside the purview of the IT department Reduce redundancies leading to better allocation of resources and increased reuse of models Figure 2 – ModelOps in your AI tech stack By providing information and insights tailored to business leaders, ModelOps solutions address one of the most pressing issues with AI adoption today. This transparency into AI usage across the enterprise provides explainability for models in a way business leaders can understand. Bottom line: ModelOps promotes trust, which leads to increased AI adoption.

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