Category: Cloud

Categories
Cloud

Observability: Looking beyond traditional monitoring

Gain critical insights into the performance of today’s complex cloud-native environments​

As businesses transition towards multi-layered microservices architecture and cloud-native applications, they often struggle to gain granularity with the traditional monitoring tools. In the traditional method, teams use separate tools to monitor the logs, metrics, events, and performance, hindering unified analysis. Monitoring tools do not give the option to drill down and correlate issues between infrastructure, application performance, and user behavior. Teams often use logs for debugging and performance optimization, which becomes very time-consuming. Static dashboards with human-generated thresholds do not scale or self-adjust to the cloud environment. As thousands of cloud-native services are deployed on a single virtual machine at any given time, monitoring has become cumbersome. Further, conventional monitoring relies on alerting only known problem scenarios. There is no visibility into the unknown-unknowns – unique issues that have never occurred in the past and cannot be discovered via dashboards.​

Businesses need to make their digital business observable such that it is easier to understand, control, and fix.  Hence, they must​ look beyond traditional monitoring. With observability, businesses can gain critical insights into complex cloud-native environments​.​ Observability enables proactive and faster discovery and fixing of problems, providing deeper visibility about issues and what may have caused them.


With observability, businesses can gain critical insights into complex cloud-native environments​.​

Categories
Cloud

Explainable Machine Learning (ML) models demystified

Enable 5X transparency in AIOps, achieving a more reliable and accurate business outcome

Service providers in the connectedness vertical embrace Artificial Intelligence for IT Operations (AIOps) to transform their businesses, but the users are hesitant in entrusting their operations to a complexly driven platform that provides no clarity and visibility into its functionality. Due to the lack of transparency, service providers are concerned about making bad decisions based on AI recommendations and the liability of such decisions and actions.

In their quest for autonomous operations, service providers seek to be more proactive with predictive analytics, where the machines make most of the decisions and help engineers take preemptive actions. However, the engineers need to have complete visibility into the underlying logic used by the AIOps and the ability to validate if the outcome is reliable.

Figure1: Assisted Artificial Intelligence and Machine Learning Framework


To accelerate AI/ML model development with enhanced transparency, enterprises must switch from existing auto-machine learning to assisted AI/ML framework-based solutions.

Explainable Machine Learning (ML) models aim to solve this problem by explaining the logic of the AIOps solutions so that the users can easily understand the outcome. The model explains the application of the AI solution and its result to the users in a way that they can clearly understand, rely on, and trust the outcome. Explanation in the ML model can be viewed as a means to transforming a black-box AIOps into a glass-box AIOps, by precisely lifting the veil on its computing and logic.