The AI Factory Model
Given the challenges in the existing state of AI adoption across industries, there’s an ongoing revolution to deliver AI platforms that offer built-in governance, security, lineage, bias detection, and explainable AI
. These are necessary components for making more trusted AI. To that end, AI Factory is a well-organized model that works on existing fundamental principles to deploy AI across departments successfully. It combines key factors such as platform, data, people, and process to deliver on the pre-defined goals and nurture learning and growth.
Not much different from a physical factory that creates physical products at scale productively and reliably, an AI Factory model can deliver AI solutions to businesses at scale. It brings the best of data, people, products, and processes together to tread beyond experiments and deliver practical AI solutions that bring value.
Building on the premise of creating a robust information architecture for sustainable AI success, AI Factory combines everything from CloudOps and DataOps to MLOps and PeopleOps.
Cloud Operations (CloudOps) is the common practice of managing the delivery and optimization of IT services in a cloud environment. This serves as the foundation for the rapid development and deployment of all AI-enabling technologies that help businesses in remote monitoring and enabling factory automation systems.
DataOps is a set of collaborative data management practices that improve communication and integrate automated data flows between managers and consumers in an AI Factory.
Security Operations (SecOps) is a collaborative endeavor between the Operations and IT Security teams for protecting corporate assets. This program can help organizations predict outages in an AI Factory and actively identify security vulnerabilities.
Artificial Intelligence Operations (AIOps) bring together the best of big data and machine learning to bring AI to factory automation. It helps automate various IT operations, including anomaly detection, event correlation, and casualty determination in an AI Factory environment.
Machine Learning Operations (MLOps) is a set of practices for efficiently deploying AI-based models in production, and in an AI Factory, it's the methodology of choice for managing internal processes.
PeopleOps is the management of labour within an organization that in an AI Factory refers to a common pool of AI-ready skills. With it as a core part of the AI factory model, tasks such as workforce management that were largely repetitive can be automated.
The AI factory model leverages multi-cloud build and deployment strategies and container-based architectures, and no-code dashboards to reduce time to production. Furthermore, persona-based collaborative platforms include various popular tools that can speed up the co-creation process. Data virtualization, again, helps in reducing integration requirements.
DataOps, ModelOps, and MLOps complement the existing DevOps cycles by bringing in the AI perspective of automation. With enough experimentation, its applications can range all the way from support provisioning and risk management to pollution monitoring and manufacturing maintenance.