The Power of Scaling Computer Vision Applications

AI is coming to the edge, especially for enterprises seeking scalable solutions. Why? Businesses are starting to realize that general-purpose foundation models are too difficult to run, too expensive, or aren’t addressing their real business needs. They have begun looking past these massive scaling models to more practical and use case-specific computer vision models.

As your enterprise grows, you need technology solutions to scale with you at every step. A recent Gartner report, "Emerging Tech Impact Radar," states that a “productivity revolution” is occurring in the AI field, and enterprises need to keep up. Scalable AI solutions are one way to stay ahead.

This article delves into what it means to be truly scalable, why scalability is paramount for enterprise-ready solutions, and how vision AI platforms greatly expedite the scaling process.

What is a Scalable Computer Vision Solution?

Computer vision is a subsection of artificial intelligence that allows cameras to detect objects, people, and events in real time. Giving cameras the capability to "see" and interpret the world as humans do makes businesses more productive, efficient, and safe. The more they can scale these impactful vision AI solutions, the more successful they’ll be.

Scalability is all about allowing an AI solution to reach its full potential. How good is its ability to deal with increasing amounts of data and resources without compromising performance? Adaptable solutions are key as businesses inevitably grow and shift.

However, scaling computer vision models in the real world is easier said than done. Advanced computing infrastructure is needed to process continual visual data, making deployment and maintenance tedious. Keeping up with technical updates and innovations in the field can also present significant hurdles to scaling. High expenses and sluggish performance often impede computer vision applications' true potential and performance.

Effective AI scalability occurs in three dimensions: horizontally, vertically, and organizationally. Horizontal scalability involves increasing the number of application deployments to thousands of cameras across various sites. Vertical scalability entails expanding the range of applications across one or several cameras and locations. Organizational scalability means fostering collaboration among teams within a secure and integrated environment.

Scaling in these three dimensions can be tedious and time-consuming. Luckily, user-friendly computer vision platforms can assist enterprises with scaling AI solutions quickly and easily.

How Do You Build Computer Vision Solutions?

Building computer vision applications is complicated and time-consuming, but easy-to-use platforms make it accessible for business leaders across all sectors.

The initial step is creating a dataset, which involves gathering data from video streams, often leveraging existing cameras. Images are then extracted from the video and annotated to identify objects or people for detection. The annotated dataset is then used to train a computer vision model, which is subsequently evaluated on its ability to detect those annotated objects or people.

Once the model is performing well, the application can be developed. Like the car body around an engine, the application empowers the model, or “car," to “drive” in the real world. The next step is to deploy the model to an edge device or the cloud, depending on which best fits the use case.

After deployment, the model's performance and accuracy are continually monitored so adjustments or retraining with new data can be done as needed. This continuous cycle of deployment, monitoring, and improvement ensures the ongoing effectiveness of vision AI solutions.

An end-to-end computer vision platform provides the tools for businesses to scale applications easily and quickly, which is of great value for enterprises needing real-time visual insights into operations. The platform approach avoids the complex, isolated silos and expenses that often occur when manually building and scaling applications from scratch.

These comprehensive, unified platforms are flexible and allow for easy scaling, allowing businesses to begin building at any point in their computer vision journeys. Platforms that are hardware agnostic and allow for integration with other tools allow for even more versatility. Each step of the application process can lead to scaling with a computer vision platform.

How to Scale Data Collection and Annotation

Manually annotating data for computer vision applications is incredibly laborious and time-consuming. However, platforms offering semi-supervised learning technology greatly streamline this process.

Semi-supervised learning takes a small amount of labeled data and combines it with a large amount of unlabeled data to create a working model. The model familiarizes itself with the data distribution, becomes proficient on the target use case, and automatically annotates the customer’s dataset. This feature reduces the annotation workload by up to 80%, as users only have to validate the model’s suggestions instead of annotating images themselves.

Alongside semi-supervised learning, synthetic data can also speed up the construction of high-quality datasets. Synthetic data (digitally-generated images) can be combined with your existing data to create datasets more rapidly while maintaining quality. Synthetic data is especially beneficial when trying to capture rare or hazardous events.

For instance, if you want to train a model to detect fires on a mining site, you can create a realistic image of fire for the model to detect. Synthetic data helps you build robust, high-quality datasets that streamline model training.

Additionally, dataset management tools enable you to capture, organize, and annotate data within one intuitive dashboard. You can integrate existing datasets and distribute workloads evenly across your team with collaboration features.

Ultimately, leveraging semi-supervised learning, synthetic data, and dataset management tools helps businesses scale practical applications faster than ever before.

How to Scale Model Training

Training and scaling computer vision models is an ongoing process. As environments evolve, vision AI models require iterations to ensure optimal performance. Physical changes are typical across all industries — warehouse lighting can change, restaurants can switch layouts, and retail stores are constantly updating inventory.

Having tools that can keep up with these changes is crucial for scaling solutions. For example, Robust MLOps has features like the ability to bring your own architecture, early stop training, and model performance analytics allowing you to update trained models or applications without starting over.

Bringing your own architecture or using transformer-based or multimodal architectures is essential for keeping up with the demand for more and larger GPUs. For instance, using RT-DETR (real-time DETR) enables users to train models with less computational power while maintaining performance.

A hardware-agnostic platform also allows users to train their model using any hardware provider, like AWS or Oracle, enabling them to utilize more substantial GPUs. This feature allows businesses to deploy and scale AI applications more efficiently.

Secondly, early stopping automatically stops model training once it reaches a point of diminishing returns, optimizing performance and preventing overfitting (when the model’s capacity to generalize across data is reduced).

Finally, model performance analytics give users critical insights into the functionality of their model. Analytics like an F1 score, performance by class, performance by size, and performance by image quadrant help to scale your vision AI project. The faster you can optimize model performance, the faster you can develop and deploy your application.

How to Scale Application Development

A critical aspect of application development is having a comprehensive set of APIs. APIs provide the functionality that powers computer vision solutions and significantly reduce the code needed to build applications. However, they are incredibly time-consuming to build. For enterprises aiming to rapidly scale, choosing platforms that provide a wide range of APIs is essential.

How to Scale with Remote Deployment

Remote deployment is essential for scalability. By enabling businesses to manage models, devices, and applications from anywhere, they can capture vital real-time data to make informed decisions.

Remote management allows you to deploy applications to edge devices or the cloud and monitor their status in real time. Teams can work on devices and applications from any location, fostering collaboration and allowing them to seamlessly make updates.

Businesses can manage devices and applications in various locations and make real-time adjustments as needed. This kind of control makes vision AI solutions truly scalable.

Another key component of scaling vision AI applications is security. Remote deployment offers protected remote access to devices with simple CLI commands and encrypts data in transit.

Additionally, statistics on performance and remote system rebooting both enhance control and troubleshooting abilities. These remote deployment features collectively contribute to successful scalability.

How to Scale with Analytics

Most enterprises are not short of data, with some estimates suggesting that the average enterprise stores over 23 billion files (or 10 petabytes) of data. But, data is just data unless you can digest and contextualize it in a visual format.

Platforms that package data into meaningful analytics are the most valuable. Customized, actionable, real-time insights empower businesses to manage their operations on another level.

Some platforms even allow businesses to integrate their existing analytics tools — augmenting their ongoing analytics procedures. Analytics are key for scaling computer vision solutions successfully.

Conclusion

The world of AI is at an inflection point, and navigating the complexity of this shift can be daunting for businesses. Managing and optimizing many applications and models across several locations requires advanced data and infrastructure. On top of that, scaling applications and computer vision while maintaining security adds another layer of complexity and time.

This is where a comprehensive, end-to-end computer vision platform can be transformative for businesses, managing and scaling the entire vision AI application building process from one place.

If you want to create a new project from scratch, update an existing application, or scale an existing application, computer vision platforms are the solution.