Cloud computing, artificial intelligence, and internet-connected devices are the ineliminable technological pillars of contemporary digital society. However, a greater untapped potential, that can usher in the next generation of digital transformations and innovations, lies latent at the convergence of these technologies.
The combined power of AI and IoT collectively referred to as the Artificial Intelligence of Things or AIoT, promises to unlock unrealized customer value in a broad swath of industry verticals such as edge analytics, autonomous vehicles, personalized fitness, remote healthcare, precision agriculture, smart retail, predictive maintenance, and industrial automation.
In principle, combining AI with IoT seems to be the obvious logical progression in the evolution of these technologies. In practice though, building an AIoT solution is fraught with seemingly insurmountable architectural and engineering challenges. In a three-part series, I will discuss such challenges in sufficient detail and address them by proposing an overarching architectural framework. This series will give you the adequate architectural context and perspective needed to build an industrial-grade scalable and robust AIoT application. Here is the series breakdown:
Part 1: AIoT Architecture — In this section, you will get a thorough grounding in the AIoT problem space, understand the inherent challenges and investigate emergent behaviors. I will present a set of effective solution patterns that can address such challenges, along with a comprehensive reference architecture. The reference architecture will serve as a cognitive map in the hitherto uncharted territory of AIoT architectures. It will assist you in pairing AIoT problem scenarios with applicable solution patterns and viable technology stacks.
Part 2: AIoT Infrastructure — Here using the reference architecture you will see how to establish an edge infrastructure for an AIoT application. The infrastructure is built using various CNCF open-source projects from the Kubernetes ecosystem such as K3S, Argo, Longhorn, and Strimzi. You will see how to configure and install these projects on a cluster of AI acceleration equipped single-board computers such as NVIDIA® Jetson Nano™ and Google Coral Edge TPU™.
Part 3: AIoT Design — In the concluding part, you will see how to design and build an AIoT application that simulates an industrial predictive maintenance scenario. In this scenario, analog sensors monitor an induction motor by sensing its power utilization, vibration, sound, and temperature, and this data is then processed by an AIoT application. This application powered by a TPU accelerator applies a logistic regression model to predict and prevent motor breakdown. You will see how ML pipelines measure drift, re-train and re-deploy the model. Using various design artifacts such as event diagrams and deployment topology models you will get an in-depth view of the systems design. You will find ample code and configuration samples in C++, Go, Python and YAML. These samples will show you how to configure, code, build (ARM64 compatible), containerize (distroless), deploy and orchestrate AIoT modules and services as MLOps pipelines across various heterogeneous infrastructure tiers. This section also includes IoT device firmware code along with circuit schematics.
Read the complete series on Cisco Tech Blog.
Be the first to comment