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AI Ops Self-Healing Framework for Streaming Pipeline Reliability

AI Ops Self-Healing Framework for Streaming Pipeline Reliability

A media streaming company headquartered in Los Angeles, USA, operated a large-scale content delivery pipeline spanning multiple data centers and cloud platforms. Frequent ingestion and transcoding failures disrupted live stream availability, increasing downtime and operational effort. To resolve this, the client collaborated with our engineering team to deploy an AI Ops self-healing automation system that proactively detected, predicted, and fixed pipeline issues before they caused user-impacting disruptions.

Image representing case study

0.4%

Stream interruption rate (global)

60%

Reduction in manual recovery efforts

92%

of issues auto-healed within 2 minutes

About  

Problem Statement

  • Frequent failures during media ingestion and transcoding caused service interruptions.
  • Manual restarts delayed recovery and consumed engineering bandwidth.
  • Static monitoring dashboards failed to predict recurring errors.
  • No automated rollback or retry mechanism for failed jobs.
  • Scaling and recovery lacked orchestration across multi-cloud pipelines.

Industry: Media Streaming & Entertainment

Services: AI Ops, Predictive Maintenance, Self-Healing Automation

Region: United States

Our Tech Stack

Tech stack we used

Solution Approach

  • Collected six months of pipeline logs and metrics across ingestion, encoding, and CDN distribution nodes.
  • Built LSTM-based predictive models to identify failure-prone tasks and detect performance degradation early.
  • Integrated Apache Airflow with AWS Step Functions to orchestrate automatic job retries and resource reallocation.
  • Deployed containerized services in Kubernetes, enabling automatic pod restarts during failures.
  • Configured Prometheus for live metric scraping and anomaly alerts tied to self-healing triggers.
  • Implemented Elasticsearch dashboards to visualize correlation between model predictions and real incidents.
  • Added fallback logic in Step Functions for rollback + failover to standby servers.
  • Integrated PagerDuty API to notify engineers only if the system could not resolve the issue autonomously.

Benefits

  • Reduced stream interruption rate to below 0.4% globally.
  • Achieved near-zero downtime during transcoding or distribution errors.
  • Minimized human intervention with automated recovery and failover.
  • Improved SLA compliance and user satisfaction.
  • Established fully autonomous, predictive pipeline reliability management.

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