Edge Computing+ AI Confluence: Get Ready to “Edgify” Your IT and Automation Operations

Edge Computing+ AI Confluence: Get Ready to “Edgify” Your IT and Automation Operations

Edge Computing enjoys a very respectable place in the current IT Transformation journeys. Together with AI, Machine Learning, IoT and Robotic Process Automation, Edge has become the most-discussed topic among global CIOs and IT leaders. According to Forrester’s Predictions 2020: Edge Computing, the ‘edgification’ of IT and Automation will become a predominant factor of differentiation between the leaders and laggards in the Cloud Infrastructure and Cloud Computing landscape.

Oh, Edge– How Great You’re!

IT systems that helped you sail through the challenges last decade are in no shape to assist your business goals. ITOps have evolved significantly, and today CIOs place a much larger emphasis on ease of deployment, speed, security and scale of automation using emerging technologies. The need for speed in Cloud and placing the Cloud on the ‘edge’ are popular strategies that have gained massive traction in the IT and ITOps industries.

According to a McKinsey & Company report, “Edge computing represents a potential value of $175 billion–$215 billion in hardware by 2025.”

As connected devices proliferate into our lives, Edge capabilities would become a norm to sustain automation within IT infrastructure. It’ll impact both Software and Hardware part of the business operations, such as SaaS, PaaS, IaaS and DaaS. we can expect a new sector growing within the Cloud – ‘Edge Computing’, says Mckinsey & Company.

To understand how Edge Computing is shaping up for the future, we sat down with FogHorn‘s leadership. In Part One of our “Edge Computing+ AI Confluence” discussion with FogHorn, Sastry the Chief Technology Officer, Sastry Malladi shared his forecasts.

The Industry Will Refine the Definition of “Edge Computing”

This year many industry players led conversations regarding the exact definition and various locations of the edge.

Organizations have struggled to understand the precise location of the edge when, in reality, the location is highly dynamic and varies by industry and use case. For example, telecom operators consider the edge of the telecom network the true edge (also called the service edge), whereas application developers and industrial plant operators define it as the point of data production (or the location of the asset being monitored).

The Telco Definition

The Telco definition of the Edge also aligns with MEC (Multi-access Edge Computing).

Moreover, some solutions adopted edge terminology without considering its exact characteristics, thus introducing more confusion to the market.

Weak Edge versus a ‘True’ Edge

Weak (or fake) edge solutions lack the ability to optimally run analytics and machine learning models on the live streaming data in a constrained compute environment, 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, rather than processing data at the Edge.

Lastly, confusion regarding the Edge-Cloud relationship. Edge is certainly complementary to cloud, although in the industrial sector, edge greatly enhances the Cloud adoption and value. Indeed, over the next year, edge computing leaders will continuously work to evolve and refine answers to questions such as: where is the edge located, what is edge computing; and why is the edge important.

Automotive Manufacturers Will Look to Edge Computing to Improve and Accelerate Autonomous Operations

Cars generate significantly more data today than ever before, and it is a big challenge to gather, merge, process, and deploy all that sensor data efficiently. The future of transportation with autonomous vehicles (AV) depends on creating the required intelligence and processing to build and operate sophisticated, autonomous systems.

For example, many AVs are expected to be electric cars, and these will require substantially more in-vehicle intelligence and system life cycle management. These are needed to maximize the efficiency and lifespan of the 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 existing edge computing applications now available 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.

Read Also: Are IoT Devices and Automatic Software Updates Prepared to Dodge Ransomware Attacks?

Time to Become an Edge-First Organization

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, and as a result, organizations miss critical insights as they’re only looking 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 (versus cloud-heavy, expensive, retroactive insights).

Organizations Will Experience a Shift From Cloud Only to Cloud-Edge Hybrid Strategies to Enable Edge AI and Iterative ML Modeling and Ongoing Improvement of Outcomes

By running ‘edgified’ versions of ML models in real-time, organizations enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source. This ensures a harmonious interplay of edge and cloud, leveraging the strengths of each ecosystem.

Indeed, in the next few years, more than 40% of organizations’ cloud deployments will include edge computing to address bandwidth bottlenecks, reduce latency, and process data for mission-critical decision support in real-time. These edge-powered, IIoT 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 as we know it. In short, 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 Edge Computing to Edge AI Solutions to Deliver Optimal ROI

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, meaningful data and the learning sets updated.

For example, in a factory, a model can be deployed to detect defects on a part inspection assembly line or proactively identify patterns that may lead to defects after a period of time. Often, after a few months, the model’s accuracy may diminish due to new data patterns. This can be misleading, and the opportunity cost can be significant if the software uses traditional analytics exclusively.

Using the power of artificial intelligence (AI) at the edge and self-learning models, in 2020, ML models can move beyond traditional analytics capabilities and significantly improve predictive functionality and overall ROI. With edge AI, the software can proactively interface with live data streams and cater to intelligence at or near the source, leading to increased overall productivity, efficiency, and cost-savings.

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