> For the complete documentation index, see [llms.txt](https://docs.elastiflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.elastiflow.com/data_platforms/elastic/ml/perf.md).

# Network Performance

Ensuring the performance of network services is critical in today's fast-paced and interconnected world. High-performing networks are vital for the smooth functioning of various applications and services, impacting everything from business operations to user experience. Poor network performance can lead to decreased productivity, customer dissatisfaction, and can even have financial repercussions. In this context, the ability to quickly identify and address performance-related issues is not just beneficial but essential.

ElastiFlow provides a collection of anomaly detection jobs designed for identifying network performance-related issues, playing a pivotal role in achieving this objective. These jobs leverage advanced analytics and machine learning algorithms to monitor network traffic, resource utilization, response times, and other performance indicators in real time.

#### Downloads

| Schema    | Link                                                                                                                                                    |
| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **CODEX** | [All Performance ML Jobs for CODEX Schema](https://github.com/elastiflow/elastiflow_for_elasticsearch/raw/master/ml/codex/performance/performance.json) |
| **ECS**   | [All Performance ML Jobs for ECS Schema](https://github.com/elastiflow/elastiflow_for_elasticsearch/raw/master/ml/ecs/performance/performance.json)     |

By deploying these anomaly detection jobs, organizations can swiftly identify and diagnose issues that affect network performance. The early detection of such anomalies enables network administrators to take proactive measures to rectify issues before they escalate into major problems, thus maintaining the health and efficiency of the network. This collection of anomaly detection jobs is an indispensable tool in the arsenal of modern network management, ensuring that networks remain robust, efficient, and capable of supporting the demanding needs of contemporary digital infrastructures.


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