Courtesy: Avnet
Myth #1: Demo code is production-ready
AI demos always look impressive but getting that demo into production is an entirely different challenge. Productionizing AI requires effort to ensure it’s secure, optimized for your hardware, and
tailored to meet your specific customer needs.
The gap between a working demonstration and real-world deployment often includes considerations like performance, scalability
and maintainability. One of the biggest hurdles is maintaining AI
models over time, particularly if you need to retrain the application
and update the inference engine across thousands of deployed devices. Ensuring long-term support, handling versioning and managing updates without disrupting service add layers of complexity
that go far beyond an initial demo.
Additionally, the real-world environment for AI applications is dynamic. Data shifts, changing user behavior, and evolving business
needs all require frequent updates and fine-tuning.
Organizations must implement robust pipelines for monitoring
model drift, collecting new data and retraining models in a controlled and scalable way. Without these mechanisms in place, AI
performance can degrade over time, leading to inaccurate or unreliable outputs.
Emerging techniques like federated learning allow decentralized
model updates without sending raw data back to a central server,
helping improve model robustness while maintaining data privacy.
Myth #2: All you need is Python
Python is an excellent tool for rapid prototyping, but its limitations
in embedded systems become apparent when scaling to production.
In resource-constrained environments, languages like C++ or C
often take the lead for their speed, memory efficiency and hardware-level control. While Python has its place in training and experimentation, it rarely powers production systems in embedded
AI applications.
In addition, deploying AI software requires more than just writing
Python scripts. Developers must navigate dependencies, version
mismatches and performance optimizations tailored to the target
hardware.
While Python libraries make development easier, achieving real-time inference or low-latency performance often necessitates
re-implementing critical components in optimized languages like
C++ or even assembly for certain accelerators. ONNX Runtime and
TensorRT provide performance improvements for Python-based AI
models, bridging some of the efficiency gaps without requiring full
rewrites.
Myth #3: Any hardware can run AI
The myth that “any hardware can run AI” is far from reality. The
choice of hardware is deeply intertwined with the software requirements of AI.
High-performance AI algorithms demand specific hardware accelerators, compatibility with toolchains and memory capacity. Choosing mismatched hardware can result in performance bottlenecks or even an inability to deploy your AI model.
For example, deploying deep learning models on edge devices requires selecting chipsets with AI accelerators like GPUs, TPUs or
NPUs. Even with the right hardware, software compatibility issues
can arise, requiring specialized drivers and optimization techniques.
Understanding the balance between processing power, energy consumption, and cost is crucial to building a sustainable AI-powered
solution. While AI is now being optimized for TinyML applications
that run on microcontrollers, these models are significantly scaled
down, requiring frameworks like TensorFlow Lite for Microcontrollers for deployment.
Myth #4: AI is quick to implement
AI frameworks like TensorFlow or PyTorch are powerful, but they
don’t eliminate the steep learning curve or the complexity of real-world applications. If it’s your first AI project, expect delays.
Beyond the framework itself, one of the biggest challenges is creating a toolchain that integrates one of these frameworks with the
IDE for your chosen hardware platform. Ensuring compatibility, optimizing models for edge devices, integrating with legacy systems,
and meeting market-specific requirements all add to the complexity.
For applications outside the smartphone or consumer tech domain,
the lack of pre-existing solutions further increases development
effort.
Myth #5: Any OS can run AI
Operating system choice matters more than you think. Certain AI
platforms work best with specific distributions and can face compatibility issues with others.
The myth that “any OS will do” ignores the complexity of kernel
configurations, driver support and runtime environments. To avoid
costly rework or hardware underutilization, ensure your OS aligns
with both your hardware and AI software stack.
Additionally, real-time AI applications, such as those in automotive
or industrial automation, often require an OS with real-time capabilities. This means selecting an OS that supports deterministic execution, low-latency processing, and security hardening.
Developers must carefully evaluate the trade-offs between flexibility, support, and performance when choosing an OS for AI deployment. Some AI accelerators require specific OS support.
What’s Next for AI at the edge?
We’re already seeing large language models (LLMs) give way to
small language models (SLMs) in constrained devices, putting the
power of generative AI into smaller products.
