Scaling AI Computer Vision Models With alwaysAI

AI is at an incredible inflection point. Many enterprises are finding that general purpose foundation models either don’t address their specific needs or are expensive and difficult to run and maintain. As such, enterprises are looking to move beyond these massive foundational models to more specific and practical models. This transition is bringing AI closer to the edge, especially for industries looking for valuable and scalable solutions.

A recent Gartner report, "Emerging Tech Impact Radar," highlighted several technologies that point to this “productivity revolution” in AI.  As strong believers in the practical applications of AI solutions, alwaysAI continues to build tools and features that enhance and augment the practical capabilities of enterprises. 

We understand that as your business grows, you need AI solutions that can grow along with you. It’s why we make scalability a top priority. Whether you serve delicious sandwiches that power our bodies, manufacture the goods that run our cities, dig the mines that power our future, or sell products that power our homes, what you do is important. Your success helps power our world and success is driven by growth. 

As your enterprise grows, you need technology solutions to grow with it. Aside from delivering real-time visual data to power your business, computer vision from alwaysAI is so impactful because it is built to scale with you at every step of the process, from data collection to deployment and beyond. Our comprehensive suite of features streamlines workflows and eliminates roadblocks, ensuring your projects reach their full potential. 

In this blog, we’re tackling what it means to be truly scalable and why it is the key to an enterprise-ready, Vision AI solution.  

What Does It Mean for a Vision AI Solution to Be Scalable?

Computer vision is a branch of artificial intelligence that empowers enterprises to detect objects, people, and events in real-time. The capability to "see" and interpret the world is helping to make businesses more productive, more efficient, and safer. Therefore, the more you can scale these impactful AI solutions, the more successful you’ll be.

Scalability refers to an AI solution's ability to handle increasing data and resources without compromising performance. In simpler terms, it's about making sure these groundbreaking advancements reach their full potential. As businesses grow and change, they need adaptable AI solutions to grow and change with them. 

While it’s possible to build deep learning computer vision models with a few lines of code, scaling deep learning models to run in the real world presents a significant hurdle. Processing visual data requires heavy workloads and advanced computing infrastructure making deployment and maintenance difficult. Furthermore, as AI technology quickly advances, keeping up with technical updates and innovations can present significant challenges when trying to scale. Trying to scale on outdated or suboptimal systems can leave computer vision applications bogged down by high costs and sluggish performance.

Working with scalable AI solutions ensures that every time you need to update a model, deploy a new version of an application, or roll out a solution to a new location, you can do so easily and seamlessly without rebuilding your solution from the ground up. 

True AI scalability happens horizontally, vertically, and within an organization. Horizontal scalability refers to expanding application deployments to thousands of cameras across multiple locations. Vertical scalability refers to growing a portfolio of applications across one (or many) cameras and locations. Organizationally, scaling in machine learning refers to the ability to collaborate with teams in a secure and cohesive environment. 

As a truly end-to-end platform, alwaysAI has comprehensive features and tools to ensure enterprises can scale AI solutions at every step of the process, horizontally, vertically, and organizationally. 

What Are the Necessary Steps in Building Computer Vision Solutions? 

Computer vision is complex – but user-friendly platforms make it accessible to enterprises across industries. 

The first step in the process is building a dataset. This involves collecting data from video streams (often from cameras already installed). Images are extracted from the video and then annotated with the objects or people to be detected. Next, the dataset is used to train a computer vision model which is then evaluated on its ability to detect the same objects or people in the annotated data. Once the model is proficient, it’s time to develop your application. The application enables the model to work in the real world by providing the necessary directions about what to do with the incoming information. Once the application is developed, the next step is to deploy it to an edge device or the cloud. 

Once deployed, the model's performance and accuracy need to be continually monitored, allowing for adjustments or retraining with new data as needed. This cycle of deployment, monitoring, and improvement ensures your computer vision application continues to function effectively.

Each component of the alwaysAI computer vision platform provides tools and functionalities that allow businesses to scale applications quickly and easily making it incredibly useful to enterprises needing real-time visual insights. 

Without the alwaysAI platform, building, deploying, and scaling Vision AI solutions is complex and potentially expensive. Each step in the model and application development and deployment would likely require its own unique hardware and software infrastructure, creating isolated silos needing individual integration and maintenance. This siloed approach multiplies complexity and maintenance especially as you begin to scale.

alwaysAI addresses these challenges by providing a comprehensive and unified platform ensuring seamless scalability. However, it is also highly flexible allowing you to jump into the platform at any step in your computer vision journey. Whether you have a dataset you want to import, a developed model you want to train, or a fully built application you want to deploy, you can easily integrate existing tools or application components into the alwaysAI platform. Additionally, the alwaysAI platform is hardware agnostic for even more versatility. 

This robust infrastructure fosters synergies between applications, reducing the cost per application as you scale, and streamlining the entire computer vision lifecycle. 

Scale Data Collection and Annotation With Synthetic Data and Semi-Supervised Learning

Traditionally, annotating data for computer vision projects is a time-consuming and laborious task. alwaysAI streamlines this process with our semi-supervised learning technology. Semi-supervised learning utilizes a small amount of labeled data to guide the learning process. This data is then combined with a large amount of unlabeled data to build a working model, ultimately learning the shape of the data distribution and becoming proficient on the target use case. The model is then used to automatically annotate the customer’s dataset, reducing the annotation workload by up to 80%. 

Instead of meticulously labeling every image, users simply validate the system's suggestions. This frees up valuable resources and significantly accelerates the development cycle. 

Therefore, as you begin to scale Vision AI solutions, you’ll need more and larger datasets. In terms of scaling, semi-supervised learning is advantageous for several reasons. 

1. It requires less computational power than self-supervised learning making it cheaper and faster.
2. It requires less resources than traditional manual annotations making it cheaper and faster. 
3. It helps create more specialized datasets, making it more practical for real-world business use cases. For example, identifying highly specialized manufacturing tools and materials is quick and easy with semi-supervised learning as it only takes a quick verification to label unique objects.   

In addition to semi-supervised learning, alwaysAI also employs the use of synthetic data to augment and speed up the process of building quality datasets. Synthetic data (digitally-generated images) can be used in tandem with your data to build datasets more quickly without losing quality which ultimately helps you scale practical applications faster. The use of synthetic data is particularly useful when trying to capture rare or dangerous events. For example, if you want to detect fires in a warehouse, you can create a realistic image of a fire for the the model to detect. With this generative data, you can create robust datasets to speed up model training without losing performance.  

Furthermore, our intuitive dataset management tools allow you to capture, organize, and annotate data all within a single, user-friendly dashboard. You can even upload existing datasets for seamless integration. For large-scale projects, collaboration features enable you to distribute workloads evenly across your team.

Optimize Model Training With Several Tools Designed for Easy Scaling

Training Vision AI models is an iterative process. As environments change, models need adjustments and refinement to continue running optimally. Retail stores may switch layouts, warehouse lighting may change, restaurant menu items may be added or removed, and countless other physical changes are common in all industries. It’s important to have the necessary tools to address these changes and create scalable, enterprise computer vision solutions. alwaysAI robust MLOps features for complex computer vision projects allow you to make updates to trained models or applications without starting over. These features include:

Bring Your Own Model (or Model Architecture)

The world of AI moves quickly. Model architectures (like YOLO) are upgraded regularly. As a result, we’ve built our platform to be as accommodating as possible to anyone, allowing users to choose from a variety of model architectures including one of our exclusive YOLO by alwaysAI model architectures, CenterNet, or RT-DETR. 

As the demand for AI solutions increases, the demand for more and larger GPUs will also continue to increase. The ability to bring your own architecture or leverage transformer-based architectures is one way alwaysAI is addressing these challenges. For example, using RT-DETR (real-time DETR) allows users to train models using computationally less power without sacrificing performance. This means enterprises can get up and running and scale AI applications more efficiently.

To truly offer best-in-class Vision AI solutions, our platform also allows users to bring any PyTorch-based architecture for incredibly versatile options. Because you can bring your own model or model architecture to the platform, you can integrate any progress you’ve already made on model development making scaling machine learning models easy.

Alternatively, users can bring their previously trained models and still leverage the incredible power of alwaysAI’s edge-optimized inference engine and deployment tools. alwaysAI supports multimodal architectures and can run on most PyTorch-based model frameworks. As a result, enterprises who have completed models do not need to “re-do” any work to leverage all the other incredible features of alwaysAI. In this way, we allow users to jump into the platform at any point without losing any work already completed. 

Furthermore, alwaysAI is hardware agnostic so you can train your model using whatever hardware provider you choose like Oracle or AWS allowing you to take advantage of more and larger GPUs. 

Early Stop Training

alwaysAI's model training features a comprehensive set of tools designed to help you train models efficiently. With early stopping, once your model reaches a point of diminishing returns, it automatically stops training, preventing overfitting (when the model’s ability to generalize across data is reduced, only making accurate predictions on its training data), and ensuring optimal performance.

The faster you can train a model, the faster you can build an application. The faster you can build an application, the faster you can deploy and scale Vision AI solutions. 

modelIQ: Model Evaluation

alwaysAI modelIQ dashboard page

alwaysAI’s exclusive model evaluation tool, modelIQ is designed to enable faster, more effective model training by providing you unparalleled insights into the critical details of your model’s performance. Understanding your model will allow you to scale your computer vision projects quickly and efficiently. In addition to an F1 score for every image in your dataset, you can also see model performance by class, size, and image quadrant. 

With this level of visibility, you can pinpoint exactly where your detections are most accurate to make more informed data adjustments and get models up and running fast. Understanding model performance more quickly, helps you move to application development and deployment more quickly. Once your application is ready to deploy, alwaysAI’s remote management allows you to scale to multiple cameras and locations with the push of a button. 

Application Development Built to Scale

One of the keys to alwaysAI’s application development is our robust library of APIs, edgeIQ. APIs help provide the functionality that powers your Vision AI solution but can be time-consuming to build. The edgeIQ library dramatically reduces the lines of code necessary to build practical computer vision applications. For users looking to scale quickly, edgeIQ is one more way alwaysAI surpasses other platforms. 

edgeIQ’s multi-stream framework further improves scalability by allowing users to build and modify multi-camera applications that can run on any type of hardware - edge, on-premise server, or the cloud. alwaysAI’s efficient framework reduces resource overhead and simplifies maintenance. 

Users can also leverage alwaysAI’s 100+ starter apps to jumpstart their computer vision projects and ultimately deploy and scale more quickly. 

Manage Growth Efficiently With Remote Deployment 

alwaysAI’s remote deployment offers significant advantages for businesses. By allowing them to manage models, devices, and applications from anywhere, enterprises can ensure they capture critical real-time data to make better business decisions.

A key feature for successful remote management is the ability to easily provision your hardware and deploy applications to edge devices or the cloud. Once the simple setup is complete, users can monitor the status of their solutions and devices, access real-time data, and maintain infrastructure remotely. Additionally, remote management facilitates distributed teams by providing control over devices and applications from any location. This enables teams to work together to control multiple versions of applications and make updates seamlessly.

This kind of control and oversight over applications and devices makes alwaysAI Vision AI solutions truly best-in-class. From one, intuitive platform, enterprises have the power to turn applications and devices on and off, make adjustments as needed, and deploy and manage applications in multiple locations. 

Features like remote system reboot and comprehensive performance statistics further enhance control and troubleshooting capabilities. These functionalities all contribute to a scalable and efficient remote computer vision deployment process.

alwaysAI remote deployment was developed to foster scalability in machine learning. We are constantly adding tools and features to continue to be a leader in developing scalable AI models and applications. 

Analytics to Drive More Intelligent Decisions

Like any source of comprehensive data, it’s only useful if you are able to visualize and contextualize the information in a digestible format. Most businesses are not lacking in data. Some estimates suggest the average enterprise stores over 23 billion files (or 10 petabytes) of data but the vast majority goes unused. 

alwaysAI doesn’t just accumulate real-time visual data but it packages that data into actionable and meaningful insights with our customizable analytics dashboard. Comprehensive, real-time insights customized to reflect your most critical needs allow you to take control of your operations like never before. 

For enterprises already leveraging analytics tools, the alwaysAI platform can seamlessly interface with all common systems - allowing you to augment your current analytics processes. Our comprehensive analytics dashboard reduces the time it takes to adopt, share, and implement the incredible real-time visual insights alwaysAI Vision AI delivers. 

Conclusion

The world of enterprise computer vision is exciting, but managing the complexity of multiple applications, models, and locations can be a real challenge. From the advanced computing infrastructure and data required to run AI applications to ensuring your Vision AI solutions are running on updated and advanced technology, trying to scale AI applications without a comprehensive platform is difficult. Security and scalability are paramount, and finding a platform that can grow with your needs is essential.

Whether you’re starting your Vision AI project from scratch, looking to update an existing model or application with new data, or simply want to scale an existing application to multiple locations, alwaysAI can help.