New Relic Introduces AI Capabilities to Enhance Observability Platform
New Relic, a leading observability platform provider, has announced the addition of new artificial intelligence (AI) capabilities to its platform. These enhancements aim to detect and resolve alert coverage gaps, which are issues that existing alerts may not be designed to address. The introduction of machine learning algorithms enables DevOps teams to identify anomalies that could indicate potential issues and provides recommended alerts for quick resolution.
Camden Swita, Senior Product Manager at New Relic, highlighted that it is common for DevOps teams to face issues that existing alerts do not cover. The machine learning algorithms offered by New Relic identify such coverage gaps and suggest alerts that the teams can customize as needed. By ensuring that the right thresholds are tracked, these algorithms eliminate blind spots that may impact application performance.
The AI capabilities introduced by New Relic leverage both predictive and generative AI. The company has been utilizing machine learning algorithms for predictive AI for several years and recently provided early access to a generative AI tool called Grok. This combination of predictive and generative AI enables DevOps teams to detect and resolve issues faster, contributing to improved application performance.
The goal of these AI capabilities is to make observability platforms more accessible to users. With the AI algorithms in place, there is no longer a need for software engineers to create complex queries to analyze metrics, logs, and traces. The AI capabilities reduce alert noise by helping users understand the root cause of an issue and minimizing unnecessary alerts.
While AI is expected to play a significant role in automating DevOps tasks, it may take time for teams to fully trust the recommendations made by AI models. These models require time to understand an IT environment and not all initial recommendations may be accurate. However, as time goes on, DevOps teams will increasingly oversee processes executed by AI models.
Different approaches exist for building large language models (LLMs) that power generative AI. Swita noted the debate between chaining LLMs together versus creating a single large parent LLM with smaller child relationships. The effectiveness and efficiency of these approaches are yet to be determined.
In the meantime, DevOps teams are encouraged to identify tasks that can be automated by AI models. Fortunately, many of these tasks are manual and not favored by most software engineers. AI automation in DevOps workflows remains a topic of interest. However, it is crucial for AI models to prove themselves by performing tasks as well as or better than humans before complete trust is placed in them.
With the inclusion of these AI capabilities, New Relic aims to enhance its observability platform and provide DevOps teams with an advanced solution for detecting and resolving issues. By leveraging machine learning algorithms, New Relic offers a comprehensive approach to identifying and addressing alert coverage gaps, thereby optimizing application performance.
In conclusion, New Relic’s introduction of AI capabilities to its observability platform represents a significant advancement for DevOps teams. These capabilities enable teams to detect and resolve issues faster, reduce alert noise, and create a more accessible observability platform. As the adoption of AI in DevOps continues to grow, New Relic is at the forefront of empowering teams to leverage the full potential of AI for improved application performance.
