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Today ’ s distributed and AI-driven systems have upped the ante for IT teams in maintaining system performance and reliability . Traditional monitoring tools , while effective in simpler contexts , often fail to provide the deep insights required in more complex settings .
Organisations are turning to observability to help capitalise on AI technologies and business applications . The New Relic 2024 Observability Forecast found that adoption of AI technologies was the top driver for observability ( 38 %) among respondents in Southeast Asia ( SEA ). Those who deployed AI-driven observability reported a 28 % higher business value and return on investment ( ROI ) overall . specific behaviours and performance characteristics of AI components .
As we navigate through an era dominated by advancements in AI technologies , it ' s clear that this technology is not only a driving force behind new applications and systems , but also a transformative component in how these systems are managed and monitored .
AI key to intelligent observability
The complexity of modern IT environments , especially those infused with AI , has outpaced the capabilities of traditional monitoring practices . AI has also become
Ned Lidbury , Director , Solutions Consulting , ANZ and ASEAN , New Relic
These findings reflect not only the growing importance of observability in supporting innovation but also that AI itself is emerging as a symbiotic force – complementing the practice of modern , intelligent observability in more ways than one .
Observability simplifies AI complexities
Observability provides a detailed view of organisational system health and performance . It involves collecting and analysing telemetry data , such as metrics , events , logs and traces ( MELT ), to understand not just what ’ s happening within a system , but why . This deeper level of insight is crucial for identifying and resolving issues in real time , ensuring that systems perform optimally under various conditions .
AI-driven systems introduce additional layers of complexity to observability . These systems often involve intricate data pipelines , model training and inference processes , and dynamic scaling based on real-time data . Observability in this context must extend beyond traditional MELT data to include the
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