In today's fast-paced business world, companies need to ensure that their IT infrastructure and estate are performing optimally to maximise productivity and profitability. AI-powered telemetry will be key to managing and optimising this increasingly complex network, positively impacting business outcomes as well as the future of work to help businesses stay ahead of the curve.
In the realm of GenAI applications, telemetry involves capturing key operational data to monitor and improve the system's performance and user experience. With its ability to rapidly process vast amounts of data, identify subtle and often hidden patterns, and make informed intelligent decisions in real-time, AI stands poised to elevate telemetry analytics to new levels of effectiveness.
In fact, when augmented with AI and machine learning, telemetry analytics goes beyond basic data processing to provide businesses with transformative operational capabilities. It offers actionable insights and automation that can facilitate predictive insights, enable preventive anomaly detection and drive intelligent closed-loop automation. This leads to more efficient operations through proactive troubleshooting. For example, Dell’s SupportAssist technology can leverage telemetry and AI to fix PC issues without human intervention. This lets IT activate Dell-authored scripts to autonomously correct blue screen errors, thermal issues and more across their entire fleet of PCs.
By leveraging AI, telemetry and automation to deliver self-healing capabilities for PCs, organisations can maximise PC uptime and improve productivity with new self-healing capabilities through the ProSupport Suite for PCs. Currently, Dell’s commercial PCs are the world’s most intelligent, with AI-enabled software like Dell Optimizer, telemetry and automation to deliver self-healing capabilities. And, we will continue to make them more intelligent.
Here are some foundational concepts of AI-powered telemetry:
Logs: Records of events that occur within an application. For GenAI, logs can capture information such as user input, model responses and any errors or exceptions that arise.
Traces: Traces offer a detailed path of a request as it moves through the various components of a system. Tracing can be invaluable in understanding the flow of data from embeddings to chat completions, pinpointing bottlenecks and troubleshooting issues.
Metrics: These are quantitative measures that give insights into the performance, health and other aspects of a system. In AI, metrics can encompass everything from request rate and error percentages to specific model evaluation measures.
There’s no doubt that telemetry serves as the backbone of a well-monitored AI system, offering the insights necessary for continuous improvement. The combination of telemetry analytics, AI and machine learning is transforming data-driven decision-making across multiple industries by acting as a catalyst for the emergence of new business processes.
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