The advent of cutting-edge technologies like artificial intelligence (AI) and edge computing has tremendous potential to enable faster decision-making via direct machine to machine (M2M) communication in an effective way without the necessity of a centralized hub.
Besides, the mix of these two technologies that is AI at the Edge helps to deliver real-time analytics, make fast decisions based on insights, create new business models and deliver better results.
AI at the Edge means running AI algorithms locally on a hardware device using edge computing. These algorithms are based on the data generated on the device itself which helps organizations to process data with the device and provides the required information in real-time.
"Edge AI Software market is forecasted to grow from $355 million in 2018 to 1.12 trillion dollars by 2023." – Forbes
In this webinar, our experts will talk about how AI at the Edge can help companies to process huge amounts of data at the device itself instead of AI processing done in a cloud-based data center with deep learning models. You will get to know how it offers speed, agility, security and intelligence in operational functions. They will also talk about advantages of having AI at the Edge and how implementing it can transform any industry.
The following are the answers to the questions that were asked during the live webinar.
Answer 1: AI at the Edge device is something like an AI chip on hardware like FPGA, ASIC, MCU’s & SOC’S. All kinds of general open-source algorithms can be used like Tensor Flow, PyTorch, Microsoft Cognitive Toolkit, etc. These models have been trained on the cloud, which need to be optimized on the edge device. All these silicon providers have their tools and software available to run the cloud-trained model locally at the hardware level.
Answer 2: AI feeds on data so it is imperative to have processes and systems in place that gather and store data, which can be used to train AI models. AI at the edge extends the capability by processing relevant and necessary information at the device level. The question should be where all your data is residing and what do you need to do to make use of it. So before starting with edge AI, you will need to kickstart your data journey. You can read more about starting your data journey here.
Answer 3: Yes. Let’s understand the use of AI at the Edge with the help of a predictive maintenance use case in factories. The sensors attached to a machine are capable of measuring vibration, temperature and noise levels. The cloud setup would mean that all the generated data would be sent to the cloud for processing. And that is where the computations take place, while the results are communicated to a centralized dashboard. At the edge, AI is performed locally and can detect the state of the equipment, potential anomalies and any indication of probable failure as defined in the AI model. There are a few approaches which can be used to map Deep Learning onto the edge.
Answer 4: Yes, our IoTConnect platform is developed on Azure and our AI models can also be integrated with the Azure IoT. You can get all the information about the platform at www.iotconnect.io.
Answer 5: Due to COVID-19 situation, we conduct the AI workshop online. It would take multiple sessions of 90-120 minutes for 3-4 days at range of different stakeholders from your organization and try to cover the pain areas, infrastructure, objectives and accordingly provide you the deliverables.