HomeData AnalyticsFogHorn on 2020: The year edge-AI helps industry finally make sense of ‘things’

FogHorn on 2020: The year edge-AI helps industry finally make sense of ‘things’

Organizations will move IoT projects from proof-of-concept to proof-of-value

Ramya Ravichandar, vice president of product management, FogHorn:

“During proof-of-concept deployments in the last few years, many organizations have confirmed the benefits that IoT can bring to a wide variety of industries – and IoT spending is expected to reach $1.1 trillion by 2025, according to IDC. [But] one challenge is figuring out how to capture, organize, process, and deploy large amounts of complex data more effectively.

“Although organizations are able to implement IoT solutions and gather data, there are still a few roadblocks impacting wide-spread adoption, including skill gaps and cloud costs. In 2020, we will see organizations move IoT projects from proofs to full deployments, with the goal to increase overall operational efficiency.

“They will focus on innovative new opportunities, such as edge computing, to drive significant ROI, deliver enhanced operational productivity, and achieve the final proof-of-value phase.”

Organizations will improve data quality to drive actionable insights 

Ramya Ravichandar, vice president of product management, FogHorn:

“While many see connectivity limitations, security risks, and data bias issues, including data quantity, as roadblocks to IoT success, data quality also plays a critical role in delivering effective IoT projects. Organizations can only make the right data-driven decisions if the data used is correct and suitable for the use case at hand.

“Edge computing plays an essential role in evaluating and delivering heightened data quality, as edge-enabled solutions can perform real-time analysis of disparate data streams and identify only the most valuable insights for further processing and AI training.

“Data processing and enrichment at the edge will contribute to IoT success by identifying and addressing false and inaccurate machine learning models that lead to machine failures, declining productivity, and cost issues.”

Edge-enabled solutions will power a more sustainable future

Ramya Ravichandar, vice president of product management, FogHorn: 

“In 2020, we will see an increase in edge computing deployments driving green tech use cases to minimize carbon footprint. Transport organizations will start deploying edge computing to detect abnormal regen and idling events in real-time to save billions of pounds of CO2 emissions per year. Oil and gas organizations will deploy edge technologies to monitor flare stack health to understand emissions output.

“Through sensor fusion technology, edge solutions will help identify issues with compressor health and alert operators about potential regulatory violations. Steel manufacturers will look to edge computing to save millions of tons of CO2 emissions by identifying defective parts produced in steel manufacturing as early as possible to reduce scrap and increase yield.”

The industry will refine the definition of ‘edge’

Sastry Malladi, chief technology officer, FogHorn: 

“Organizations have struggled to understand the precise location of the edge when, in reality, it is highly dynamic, and varies by industry and use case. Telcos consider the edge to be the edge of the telecom network – also called the ‘service edge’. This aligns with the MEC (multi-access edge computing) definition. But application developers and industrial plant operators define it as the point of data production – or the location of the asset being monitored.

“Some solutions have adopted edge terminology without considering its characteristics, thus introducing more confusion. Weak (or fake) edge solutions lack the ability to run analytics and machine learning models optimally on streaming data in a constrained compute environment – which is a crucial requirement for deriving actionable insights in real-time. These solutions are not ‘true edge’ as they rely on the cloud for data processing.

“There is confusion about the edge-cloud relationship, as well. Edge is certainly complementary to cloud, although in the industrial sector, edge greatly enhances the cloud adoption and value. Over the next year, edge computing leaders will continuously work to evolve and refine answers to all of these questions.”

Car makers will look to edge computing for real-time vehicle functionality 

Sastry Malladi, chief technology officer, FogHorn: 

“Most autonomous vehicles will be electric cars, which will require substantially more in-vehicle intelligence and system life cycle management. These are needed to maximize the efficiency and lifespan of battery and charging systems, as well as other systems supporting braking, motor performance, safety, passenger environment, and predictive maintenance.

“While fully autonomous vehicle controls are years away, there are many edge computing applications available now to enhance the efficiency, reliability, and safety of commercial and public transportation. These include vehicle control and safety systems, such as cameras, driver assistance, and collision avoidance functions, that are being added to new vehicles every year.

“In the year ahead, rather than relying on remote data centers for critical command and control decisions, automotive manufacturers can eliminate safety concerns and fast-track the road to autonomous driving by deploying edge-enabled systems.”

Organizations will shift to hybrid edge-cloud strategies to enable AI and ML modeling

Senthil Kumar, vice president of software engineering, FogHorn: 

“Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations. Organizations often depend on down-sampled or time deferred data to avoid significant cost constraints. As a result, organizations miss critical insights as they’re looking only at incomplete datasets.

“Instead, by implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities. By running ‘edgified’ versions of ML models in real-time, they can enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source.

“Edge-powered industrial IoT projects will extract a realistic view of daily machine operations and work towards a new level of predictability that will dramatically alter the industry landscape. In 2020, cloud-dominated solutions will adopt a more edge-first, or cloud-edge hybrid, approach to drive significant business value.”

Organizations will look beyond towards edge AI solutions to deliver optimal ROI

Senthil Kumar, vice president of software engineering, FogHorn: 

“When organizations build ML models, an assumption is made that the model will be accurate for a certain period of time, as the model has been trained on a particular set of data. If new data patterns emerge or if the model has not been trained on all possible data sets or workflows, the model might not continue to provide accurate results. By employing edge AI, the models can be continuously updated with new data and the learning sets updated.

“For example, a model can be deployed in a factory to detect defects on a part inspection assembly line or proactively identify patterns that may lead to defects after a period of time. After a few months, the model’s accuracy may diminish due to new data patterns; this can be misleading, and the cost can be significant.

“Using AI at the edge, ML models can move beyond traditional analytics capabilities and significantly improve predictive functionality and overall ROI. With edge AI, software can proactively interface with live data streams and cater to intelligence at or near the source, leading to increased productivity and efficiency.”

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