# From Data to Insight: Helpshift's Journey with ML Observability

DevFeed: [From Data to Insight: Helpshift's Journey with ML Observability](<https://devfeed.tech/articles/from-data-to-insight-helpshift-s-journey-with-ml-observability-30515.md>)

Original publisher: [Read original article](<https://medium.com/helpshift-engineering/from-data-to-insight-helpshifts-journey-with-ml-observability-9680e27d1d01?source=rss----3229f31ca4f4---4>)

Author: Sujit Singh

Published: 2025-11-26T14:00:15Z

Content type: article

Language: en

Sources: [Helpshift](<https://devfeed.tech/sources/helpshift.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [observability](<https://devfeed.tech/topics/observability.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [inference](<https://devfeed.tech/tags/inference.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [model-monitoring](<https://devfeed.tech/tags/model-monitoring.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [observability](<https://devfeed.tech/tags/observability.md>)

## AI overview

Helpshift describes its journey toward building a custom machine learning observability solution. The article explains ML observability, outlines system, inference, and model monitoring, discusses limitations of existing tools, and introduces an approach based on "Wide Events."

## Source excerpt

Introduction In an age where artificial intelligence (AI) and machine learning (ML) are integral to almost every aspect of our lives, ensuring the effectiveness, fairness, and reliability of ML models is paramount. Observability plays a crucial role in maintaining the performance of these models, allowing us to detect and resolve issues promptly. At Helpshift, we recognized the need for robust ML observability to keep our models running smoothly and efficiently. This blog post explores our journey in building a custom ML observability solution tailored to our specific needs. We'll delve into the concept of ML observability, discuss the limitations of existing tools, and share how we implemented our own solution based on the idea of "Wide Events." Understanding ML Observability ML observability is the ability to monitor and understand the performance, behavior, and outputs of machine learning models in real-time. It enables us to proactively identify potential issues and anomalies, facilitating timely interventions and mitigating risks. ML observability encompasses several key components: System Monitoring: Tracking the performance of the infrastructure where ML services are deployed, including metrics like CPU and memory usage, network traffic, disk space, and service performance. Inference Monitoring: Evaluating and auditing the real-time performance of deployed ML models in production by tracking incoming requests and the accuracy of model predictions. Model Monitoring: Observing the long-term accuracy of ML models by monitoring key metrics such as accuracy, precision, recall, and F1-score, and detecting any drift over time. Why Observability Matters If you've ever played Age of Empires, you know how crucial it is to explore the map to manage resources proactively and strategize effectively. Similarly, ML observability is about exploring properties and patterns not determined in advance. It allows us to be proactive in debugging and improving our systems, ensuring