From smartphones and laptops to televisions, the devices we use every day are built from hundreds—sometimes thousands—of individual components. Even the smallest deviation in a single part can compromise the reliability of the entire product. In electronic component manufacturing, precision quality inspection isn’t optional—it is critical.
AI has become a core technology for achieving that precision at scale. Many leading manufacturers have already invested in internal AI teams and are actively driving AI Transformation (AX) by transitioning field engineers into AI practitioners. But executing AX is far from straightforward. As AI systems scale, so does operational complexity—and this is where an MLOps-based AI platform plays a critical role. Through a conversation with a global electronic components manufacturer using Runway, we explore what it takes to operate AI successfully in production.
Why MakinaRocks Runway?
“In an environment where we had to consider air-gapped infrastructure, security requirements, and operational constraints—and where many team members had limited AI experience—we found Runway to be the most practical MLOps platform for building and operating AI systems.”
This manufacturer had already been developing AI-based quality inspection models internally. These models analyze high-resolution images captured across production processes to automatically detect component dimensions, characteristics, and defects—improving both product quality and operational efficiency.
A structural diagram illustrating that AI systems are not just ML code, but a complex operational process where data, infrastructure, resources, and monitoring are all interconnected. Source: Google, “Hidden Technical Debt in Machine Learning Systems,” 2015.
As AI adoption expanded across production lines, the number of models in operation increased rapidly. Different components, varying process conditions, and frequently changing quality standards required separate models for each use case. At the same time, limited AI operations experience across the organization created bottlenecks—tasks like environment configuration and resource allocation became dependent on a small number of specialists.
In practice, AI systems are not just about models. They are complex operational systems where data preparation, model deployment, resource management, and monitoring account for the majority of the workload. As the complexity of model versioning, infrastructure management, and performance monitoring grew, the need for a standardized and reliable MLOps framework became clear—one that also had to meet strict manufacturing requirements, including air-gapped environments, security constraints, and on-premise infrastructure.
After evaluating multiple options, the company selected Runway as the platform capable of reliably training, deploying, and operating dozens of AI models at scale.
Enabling domain experts to become AI practitioners
“Previously, whenever we needed to deploy a model or configure resources, we had to rely on a single engineer—’Can you set this up for us?’ Now, Runway automates these processes, allowing each team to create and manage their own inference environments.”
One of the key goals of AX is to enable domain experts—engineers in design, materials, and hardware—to actively participate in AI development and operations. However, infrastructure complexity often stands in the way. Tasks like GPU configuration, environment setup, and library installation can be overwhelming for teams without a computer science background, causing operational responsibilities to concentrate in a few individuals and slow down the entire organization.
By providing automated GPU allocation, pre-configured execution environments, conflict-free job scheduling, and a web-based training and monitoring interface, Runway removes the need to manage infrastructure manually. Domain experts can train and deploy models independently, experimentation becomes faster and more accessible, and AI capabilities scale across the organization—not just within a small team. Standardizing operations also reduces dependency on specific individuals, creating a foundation for sustainable AI adoption.
Stable inference at manufacturing scale
Resource monitoring for individual models deployed on Runway’s inference service
“Our goal is simple: uninterrupted, stable inference across our internal AI systems. With Runway, we are building exactly that.”
Manufacturing environments generate massive volumes of image data that must be analyzed in real time. This manufacturer processes hundreds of thousands of images per month, and any delay or inconsistency in inference directly impacts quality decisions on the production line.
Runway integrates with the company’s internal AI systems so that incoming images are routed to the appropriate models, inference is executed immediately, and results are returned in real time. This allows multiple quality inspection models—across different processes and product lines—to operate simultaneously with high reliability. Automated model versioning and change tracking maintain consistency across deployments, supporting fast and stable quality decisions even in complex, multi-model environments.
Building high-quality data with Runway Annotation Studio
Accurate annotation is one of the most critical factors in the performance of image-based AI models. In electronic component manufacturing, a single image may contain numerous objects, each requiring precise labeling, and variation across processes makes consistency especially important. When this data-building work is connected to an MLOps environment, labels, versions, and task standards can all be managed within a single consistent flow—creating a stable foundation for operating large-scale manufacturing datasets.
Runway Annotation Studio is an industry-specific annotation software developed by MakinaRocks to meet exactly these manufacturing requirements. It functions as an extensible application that integrates directly with Runway.
“Before using Runway Annotation Studio, each engineer had to install segmentation models locally and manually label objects one by one using a separate tool. Now, clicking on an image automatically outlines the object’s boundary—making the entire process significantly faster and far more consistent.“
Runway Annotation Studio allows users to get started quickly without complex configuration. Datasets created through Runway Annotation Studio connect directly to model training and deployment stages on Runway, making it possible to run data creation and model operations as a single unified workflow rather than disconnected processes.
Governance for large-scale AI operations
Well-implemented MLOps improves both operational stability and collaboration efficiency. Before adopting Runway, models were stored on each engineer’s individual local environment—meaning that if that PC was turned off, the entire quality inspection system depending on it was affected. With Runway, AI models are centrally managed, production line models can be served stably, and monitoring makes it easy to check system status at any time.
Runway also provides the structure to maintain operational quality even in organizations with frequent team changes. When new team members join, they can pick up the same pipelines and work context without disruption, significantly reducing the burden of handover. Sharing and explaining work has also become much easier for team members with limited AI experience.
AI platform built for real manufacturing environments
“Runway is especially valuable for organizations that have struggled with building out their AI environment or establishing a solid operating framework. Companies that have found it difficult to get the basics in place—GPU server configuration, execution environment setup—will feel the benefits most. That said, even teams with strong AI experience will find clear efficiency gains as experiment management and operational workflows become more organized.
Above all, one of Runway’s greatest strengths is how flexibly it can be adapted to each company’s situation and process requirements. At the ATTENTION conference, we saw firsthand that even though every company there was using the same Runway platform, the way each one had implemented it looked completely different—like entirely separate products. That speaks to how naturally the platform scales to fit what each environment actually needs. Throughout our own journey, MakinaRocks’ team was always quick to guide us whenever we needed support, and based on that experience, we’d wholeheartedly recommend Runway to other manufacturers.”
Note: This post was translated from the original Korean version by Kyoungyeon Kim.
