Updated ml_systems

[Sara-Khosravi]

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[Sara-Khosravi]

@profvjreddi

Hi Vijay, I hope all is well! I am enjoying this chapter, and the insights you provided here are not only significant but also invaluable for the telecom industry:

"The shift from Cloud ML to Edge ML and TinyML underscores a growing trend toward distributed and localized computing. The need for faster response times, enhanced privacy, reduced bandwidth usage, and resilience in connectivity-limited environments drive this progression. It also highlights the increasing importance of specialized hardware, such as GPUs and TPUs for Cloud ML, edge-specific AI processors for Edge ML, and highly optimized microcontrollers for TinyML."

In telecom, this shift is particularly crucial. Because we deal with extremely large datasets, clusters can crash unexpectedly, making it difficult to predict issues promptly. However, predicting outages and high congestion in the network is vital for ensuring optimized performance and seamless service delivery.

The importance of this transition is further underscored by the challenges of 5G and IoT deployment. Major network outages, such as those experienced by providers like Rogers, demonstrate the need for resilient, decentralized solutions. Edge ML and TinyML, through localized processing, play a crucial role in mitigating service disruptions by enabling real-time anomaly detection and predictive maintenance. These technologies significantly enhance network reliability and improve the overall user experience by reducing reliance on centralized cloud infrastructure.

Have a wonderful Weekend!