The Zoo Revolution: Why Pretrained Models Are The Key To Scalable Edge AI

Forbes

28-04-2025

Rajesh Subramaniam is Founder and CEO of embedUR systems.
From IoT and robotics to industrial automation and smart devices, AI is fundamentally changing how machines operate. But one of the biggest hurdles to widespread adoption has always been the complexity of building, training and deploying AI models—especially on the edge.
That's where pretrained models come in. These ready-to-use tools are making AI faster, cheaper and more scalable. The rise of AI model zoos—curated collections of pre-built, optimized models—is the key to this transformation. Think of model zoos as an app store for AI that gives developers access to powerful AI capabilities (working models, curated datasets, blueprints) without a steep learning curve, extensive training, or deep technical expertise.
For many businesses, training an AI model from scratch is not practical due to cost or time constraints. Even before building a product, teams must validate feasibility, which is often the costliest step. Pretrained models can accelerate this process by enabling rapid testing of multiple ideas to see which are viable before committing resources. Instead of spending months developing a proprietary model, companies can take an existing model from a model zoo, fine-tune it for their specific needs and deploy it rapidly—especially in cloud environments where models can run with minimal changes. On edge devices, deployment is more complex and often requires additional porting and optimization for each hardware platform.
Small, low-power IoT devices at the edge need models that are both lightweight and efficient. Pretrained models have already been optimized for real-world applications; some are stripped-down versions of larger networks, making them ideal for quick prototyping on tools like Raspberry Pi. But in production, these models can be deployed on advanced, AI-native chips from vendors like Synaptics, STMicroelectronics and Silicon Labs, designed specifically for edge inference on a single chip.
Traditionally, many of these small, low-power devices rely on cloud connectivity to make intelligent decisions. But running pretrained models directly on edge devices can reduce latency, improve reliability and conserve power.
Developing high-performance AI for edge devices comes with enormous challenges. First, curating high-quality, relevant datasets is crucial. AI is only as good as the data it's trained on. This is especially important for edge AI, where a bad model can result in significant failures—for example, a facial recognition model that isn't trained on a diverse set of faces, lighting conditions and environments. Businesses that want to deploy AI need to make sure their datasets are well-curated, balanced and representative of actual use cases.
Equally important is code and model efficiency. Edge devices operate under tight constraints: limited memory, storage, processing power, and often battery life. Unlike cloud environments, where inefficient code can be masked by throwing more compute at the problem, we don't have this luxury with edge AI. You can't afford bloated models with 20% waste. On the edge, there's no tolerance for inefficiency and no room for error. Every line of code and every model parameter has to be optimized.
In cloud-based AI, an accuracy rate of 95% is often considered acceptable. But in edge AI, where devices have to operate independently with minimal errors, this isn't enough. For instance, it's not OK if a self-driving car fails to detect pedestrians in one out of every 20 trips. Achieving a required accuracy of 99% and above requires extensive testing and iterative improvements.
The next five years will bring a wave of intelligent edge devices replacing traditional electronics. These AI-powered systems will be smaller, more energy-efficient and capable of making real-time decisions without relying on cloud connectivity. This shift will affect every industry that relies on connected devices, from smart homes to industrial automation.
But there's an important dynamic that's often overlooked: product life cycles are shrinking. Over the past decade, for example, hard drives have evolved every few months instead of every few years. The same will apply to AI-powered devices. Products considered bleeding-edge today could be obsolete within months or a year, replaced by newer, more advanced alternatives. That means companies will need tools that can help them get new products to market fast and adapt to changing technologies as quickly as possible. Pretrained models and model zoos will be crucial in this race.
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