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Edgification explained: Improving operational efficiency with edge-based machine learning

Written by Sastry Malladi, Chief Technology Officer, FogHorn

Machine learning (ML) technology plays an integral part in digital transformation. By implementing ML technology, organizations can experience benefits such as increased productivity, efficiency, and cost-savings; however, there is a problem. While the cloud has merit as a data modeling and machine learning portal, it cannot always provide the real-time responsiveness critical in industries like manufacturing, oil and gas, construction, transportation, and smart buildings.

One of the more significant challenges is bandwidth, as industrial environments typically lack the network capacity to ship all collected sensor data up to the cloud. Even if they have the necessary bandwidth, the associated costs of transmitting the data are typically high, not to mention the latencies involved. Concerns of cybersecurity attacks is also a significant issue in these environments with proprietary data. Thus, cloud-based analytics are not suitable for these industrial, real-time use cases. To fully reap the benefits, operational teams need a way to analyze the high volume of diverse streaming data coming from machines in real-time with full fidelity (not downsampled) and interpret it for actionable insights.

These industries are turning to edge computing to help eliminate latency, bandwidth costs, and security issues. Edge computing helps industrial companies struggling to generate actionable intelligence from operational data by bringing big data, streaming analytics and machine learning next to the devices producing the data. Indeed, Gartner predicts that in four years, 75 percent of enterprise generated data will be processed at the edge (versus the cloud), up from less than 10 percent today.

Machine learning at the edge versus cloud

Unfortunately, moving machine learning (ML) to the edge requires more than merely changing where the processing happens. Today's machine learning models were developed with assumptions of cloud environment capabilities - seemingly limitless compute power with little to no constraints on model size and weights - which is entirely different from compute constrained edge devices. Also, the cloud-based models create significant code bloat with pre- and post-processing logic involved as part of the model, which doesn't bode well for size-restricted edge devices. Edge-based models also need to be modified to take real-time streaming data as input sources rather than batch files. Lastly, runtime environments and implementation languages also need to be carefully selected for edge environments.

The process of taking a cloud developed ML model and turning it into an optimized runnable-on-edge, is called "edgification."

Benefits of edgification

To benefit from the possibilities of an edge environment, machine learning models need to go through the edgification process, which connects the models to streaming data, extracting pre- and post-processing logic into CEP (Complex Event Processing) expressions, optimizing the model weights/layers, optimizing the model computations, and effectively utilizing the available CPUs or other processing chips on the device (FPGA, GPU, etc.).  

A crucial component of edgification is the hyper-efficient complex event processor (CEP) that cleanses, normalizes, filters, contextualizes and aligns "dirty" or raw streaming industrial data at the source. The CEP function prepares the data for optimal ML/AI performance. Without a CEP, data either remain "dirty" or requires offloading heavy compute functions to ML models, making the resulting insights much less accurate.

Edgification promises numerous benefits, including:

  • Massive reduction of data sent to the cloud. When analytics move to the edge, there is a massive decrease in the amount of data pushed across the network. This reduces data storage, data-handling, and bandwidth costs.
  • Better real-time insights. By keeping the computing close to the data source, edgified machine learning models can detect emerging patterns in real-time and enable immediate action.
  • Predictive maintenance for all. Because an edge-based system can handle all incoming sensor data, it can predict maintenance needs across all equipment in operation, allowing for a comprehensive understanding of all upcoming maintenance needs.
  • Improved yield. Manufacturers can increase productivity and reduce downtime by rapidly detecting and addressing suboptimal performance.

The move to the edge will be driven not only by the vast increase in data, but also the need for higher fidelity analysis, lower latency requirements, and substantial cost advantages. The edgification of ML models will drive adoption of more powerful edge computing for IIoT applications overall. However, the cloud will continue to play a critical role in ML model creation and training, especially for the significant computing requirements for training and creating deep learning models - creating a symbiotic relationship between the edge and the cloud.

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About the Author

 

As CTO of FogHorn, Sastry is responsible for and oversees all technology and product development. Sastry is a results driven technology executive with deep technology and management experience of over two and half decades. His areas of expertise include developing, leading and architecting various highly scalable and distributed systems, in the areas of Big Data, SOA, Micro Services Architecture, Application Servers, Java/J2EE/Web Services middleware, and cloud Computing to name a few.

Prior to joining FogHorn as CTO, Sastry was Chief Architect of StubHub, an eBay company where he led the technology architecture transformation and also spearheaded the Big Data initiatives and data driven decisions. Sastry was also a key technology executive at eBay that lead the technology re-platforming effort from its monolithic architecture to the distributed, and scalable service oriented architecture that it is today enabling the business growth. Prior to joining eBay, Sastry was co-founder and CTO of OpenGridSolutions, Founding member and Architect at SpikeSource, and an Architect at Oracle. Sastry has also worked at many other companies in his career early on.

Sastry frequently speaks at many technology conferences, contributed to many standards and has several patents under his belt. He holds a Masters degree from I.I.T, Kharagpur, India.

Published Wednesday, April 24, 2019 7:28 AM by David Marshall
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