As artificial intelligence technologies continue to develop and advance with the advances in deep learning and more powerful GPUs, businesses are taking notice. But deciding to use enterprise AI solutions is just the tip of the iceberg—in particular, you need to decide between a single-purpose AI system vs. an AI platform. In this article, we’ll discuss why AI platforms are generally more flexible, expandable, and agile than alternatives such as a single-purpose AI system.
According to a 2019 survey, 71 percent of organizations say that they plan to use more AI and machine learning in the near future, but we at Chooch AI believe that an AI platform is far preferable to a single-purpose AI system.
What is a single-purpose AI system?
A single-purpose AI system is just what it sounds like: an AI system that has been built for a single use case. This system may have been built internally, or by a third-party team of AI experts.
What is an AI platform?
An Enterprise AI Platform is a flexible, extensible framework. A platform makes it easier for businesses to develop solutions and applications using artificial intelligence. These platforms usually include assets such as AI algorithms, pre-trained models, datasets, and/or simple visual interfaces. Many AI platforms have prebuilt workflows for highly common use cases such as facial recognition, object recognition, and recommender systems.
AI platforms vs. single-purpose systems
When you need a solution to a pressing business problem, developers’ first thought is often building a single-purpose AI system. In the same vein, entrepreneurs often have a single motivating idea or application that compels them to launch a startup in the field of AI.
It’s true that the domain expertise of a single-purpose AI system (e.g. visual inspection) or service (e.g. data labeling for autonomous driving) should inform your planning, training data, model, and deployment. However, if you ask the author of e.g. a facial recognition system to reapply the product to another domain, such as e.g. identifying and counting cells, it might be nearly as time-consuming as building a new AI system from scratch. This is the biggest problem with a single-purpose AI system: it can be extremely inflexible and unable to adapt to change as your organization grows and evolves.
An AI platform, on the other hand, is intended to be suitable for a wide variety of possible use cases. This means that such a platform has to be built to adapt, expand, and extend itself over time. For larger organizations, or for organizations who anticipate making changes in the future, enterprise AI platforms make far more sense.
The functions of an AI platform
In order to be truly effective as a standalone entity, AI platforms need to wear many different hats. The various functions of a well-rounded AI platform are:
- Data collection: AI models need vast quantities of data in order to function at peak performance—the more of it the better. Using an AI platform can help automate much of the data collection and organization process.
- Annotation labeling: Most organizations use AI to perform “supervised learning”: learning from examples that are labeled (e.g. photographs of individuals). AI platforms can help create annotations and labels to prepare your dataset for training.
- Algorithm and framework selection: Different AI algorithms and frameworks are better suited for different kinds of use cases. An AI platform can help advise you on the best approach to take for your situation.
- Training: The AI training process can be long and complicated. AI platforms can provide guidance and advice on the best and most efficient way to proceed.
- AI model generation: Even after training is complete, it can be tricky to take the generated model and start using it for real-world situations. Using an AI platform can help smooth over these bumps.
- Testing: Before using an AI model in production, it absolutely needs to be tested on a fresh dataset that it hasn’t seen before to assess its true accuracy.
- Retraining: It’s very rare that an AI model functions perfectly after just a single round of training. Rather, you need to experiment with and fine-tune the results by tweaking the model and the training hyperparameters.
- Inferencing: Real-time inferencing is crucial for applications such as facial recognition and autonomous vehicles.
- Deployment to cloud or edge: Finally, AI platforms can help you deploy the finished model to wherever is most convenient for you—whether that’s servers in the cloud or on an edge ai device.
The Chooch AI platform has a variety of advantages—most importantly, the ability to construct many possible solutions, giving you greater flexibility and agility. That’s why Chooch has built its own AI platform that makes it easy for organizations of all sizes and industries to bring AI into their workflows.
Businesses use the Chooch AI platform across a wide range of AI Enterprise Solutions, whether it’s for healthcare, safety and security, retail, or manufacturing. Want to try it out for yourself? Check out our Visual AI platform and get in touch to start your free trial.