AIOps (Artificial Intelligence for IT Operations) leverages machine learning to empower IT systems to observe, process, and react with a speed and precision that surpasses human capabilities.
Our instructor-led training programmes demonstrate how to deploy AI for automating root cause analysis, forecasting outages, identifying anomalies, and enhancing incident management workflows.
Online live training is facilitated through interactive remote desktop technology, while onsite sessions in Durban offer immersive, hands-on laboratories utilising real-world alerts, logs, and telemetry data.
Whether in data centres or war rooms, onsite training can be conducted at your organisation’s premises in Durban or at a NobleProg facility specifically designed to support troubleshooting, collaboration, and system resilience.
Also referred to as AI for IT Operations or Intelligent IT Monitoring, AIOps training enables teams to evolve from manual firefighting towards predictive, data-driven operational models.
NobleProg – Your Local Training Provider
Garden Court South Beach
Durban, South Africa
Way to get there
From Durban city centre: follow the beachfront/Marine Parade south toward South Beach.
By car/taxi: give the driver the address 73 O.R. Tambo Parade, South Beach, Durban 4001.
From King Shaka International Airport: the hotel is roughly 30 km ;from the airport.
Acquire the skills to construct an end-to-end AIOps pipeline using open-source tools in this instructor-led training held in Durban. Explore observability, anomaly detection, and automated alerting via Prometheus, ELK, and custom ML models, developing practical skills for real-world production environments.
This live session for Durban guides intermediate IT teams in unifying Splunk, Moogsoft, and Dynatrace for enterprise AIOps. It explores anomaly detection, alert linkage, and process automation to lower MTTR and enhance operational efficiency at scale.
This advanced AIOps training in Durban focuses on constructing ML models for incident prediction and automating root cause analysis. Participants will design intelligent RCA workflows, integrate remediation processes, and scale AIOps pipelines in production environments through hands-on lab exercises.
This live, instructor-led training in Durban assists observability professionals in integrating AIOps using Prometheus, Grafana, and ML. Participants will configure observability stacks, apply machine learning for anomaly detection and forecasting, and establish intelligent alerting rules for automated operations.
This instructor-led training in Durban delves into AIOps fundamentals for IT operations professionals. Discover how to correlate metrics and logs, diminish alert noise, and automate incident response. Through hands-on labs, you will build the practical skills necessary for modern, intelligent observability stacks.
This programme is tailored for seasoned systems administrators operating within a regulated banking sector who are looking to shift from reactive, isolated troubleshooting methods to proactive, ITIL-aligned, observability-centric, and AI-supported operational models. It integrates in-depth technical troubleshooting (covering Linux, Windows, Oracle, and MySQL) with contemporary SRE and observability practices, as well as AIOps capabilities for anomaly detection, event correlation, and root cause analysis (RCA).
This instructor-led, live training in Durban (online or onsite) is aimed at observability and SRE engineers who want to integrate LLMs and AI into their monitoring, alerting, and incident analysis workflows.
This live, instructor-led training in Durban (available online or on-site) is designed for SRE and DevOps engineers who wish to design, build, and safely deploy AI agents for autonomous IT operations.
This instructor-led, live training in Durban (online or onsite) is aimed at ML engineers and platform teams who need to build robust operational pipelines for LLM-powered applications at scale.
This instructor-led, live training in Durban (online or onsite) is aimed at security and ML engineers who need to identify, test, and defend against attacks on ML models and LLM-powered applications.
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