AIOps Professional Training Course

Name: AIOps Professional Training Course
Price: 1799 USD
Availability: InStock

Course Overview

Enterprise-Grade AIOps Expertise

The AIOps Professional Training Course is an advanced 5-day, 40-hour program for experienced practitioners who need to design and operate AIOps solutions at enterprise scale. This course goes well beyond foundational concepts to address the complex challenges of multi-cloud monitoring, advanced machine learning model selection, and intelligent incident management across large, distributed environments.

You will tackle real-world enterprise scenarios such as correlating telemetry across AWS, Azure, and GCP simultaneously, building incident intelligence systems that automatically determine root cause across hundreds of microservices, and implementing capacity planning models that forecast resource needs months in advance with high accuracy.

The training combines advanced lectures with extensive lab work in a multi-cloud environment, ensuring you leave with production-ready skills. This course prepares you for the Certified AIOps Professional exam, with the voucher included in your enrollment.

Quick Facts

Duration: 5 Days / 40 Hours
Format: Live Virtual or In-Person
Labs: Multi-cloud lab environment
Level: Advanced
Certification: Certified AIOps Professional
Price: $1,799 (includes exam voucher)

Learning Outcomes

What You'll Learn

Advanced skills for enterprise-scale AIOps design and operations.

Enterprise AIOps Design

Architect AIOps solutions for large-scale enterprises with thousands of applications, complex dependencies, and strict compliance requirements.

Multi-Cloud Monitoring

Implement unified observability across AWS, Azure, GCP, and on-premises infrastructure with consistent alerting and correlation.

Advanced ML for Operations

Apply advanced ML techniques including deep learning, ensemble methods, and reinforcement learning to complex operational challenges.

Incident Intelligence

Build intelligent incident management systems that automate root cause analysis, impact assessment, and resolution recommendations across microservices.

Capacity Planning

Develop ML-driven capacity planning models that forecast resource consumption, predict scaling events, and optimize cloud spend.

AIOps Governance

Establish governance frameworks for AI models in operations, including bias detection, model drift monitoring, and audit trails for automated decisions.

Target Audience

Who Should Attend

Built for experienced practitioners ready to tackle enterprise-scale AIOps challenges.

Senior SREs designing observability strategies for complex distributed systems
Lead DevOps Engineers responsible for multi-cloud monitoring architectures
AIOps Engineers seeking to advance beyond foundational implementations
IT Operations Leads managing enterprise monitoring platforms

Cloud Architects integrating AIOps into enterprise cloud strategies
ML Engineers applying machine learning to operational data at scale
Platform Engineers building internal AIOps capabilities
Certified AIOps Engineers ready for the professional level

Course Details

Training Details

Duration

5 Days / 40 Hours
8 hours per day

Format

Live Virtual Classroom
or In-Person

Lab Environment

Multi-cloud labs
(AWS, Azure, GCP)

Prerequisites

AIOps Engineer cert
or equivalent experience

Curriculum

Course Curriculum

Five days of advanced content covering enterprise AIOps at depth.

Module 1: Enterprise AIOps Design

Enterprise observability architecture patterns
Service dependency mapping at scale
Federated vs. centralized AIOps models
Handling high-cardinality telemetry data
Lab: Designing an enterprise observability blueprint

Module 2: Multi-Cloud Monitoring

Unified telemetry collection across AWS, Azure, and GCP
Cross-cloud correlation and topology discovery
OpenTelemetry for vendor-neutral instrumentation
Cost optimization for multi-cloud telemetry storage
Lab: Building a multi-cloud monitoring pipeline

Module 3: Advanced ML for Operations

Deep learning for log anomaly detection (LSTM, Transformers)
Ensemble methods for robust metric classification
Reinforcement learning for adaptive auto-scaling
Transfer learning across operational domains
Lab: Training and deploying an ML model on ops data

Module 4: Incident Intelligence

Automated root cause analysis with causal inference
Impact radius assessment and blast zone mapping
Intelligent incident routing and war room automation
Post-incident analysis with ML-driven insights
Lab: Building an incident intelligence engine

Module 5: Capacity Planning

ML-driven capacity forecasting models
Seasonal decomposition and trend prediction
Right-sizing recommendations with cost optimization
Proactive scaling triggers and threshold automation
Lab: Building a capacity forecasting dashboard

Module 6: Governance & Compliance

AI model governance for operational decisions
Model drift detection and retraining pipelines
Audit trails for automated remediation actions
Regulatory compliance (SOC 2, GDPR, HIPAA) for AIOps
Lab: Implementing governance controls and model monitoring

Certification Prep

Prepare for the Certified AIOps Professional Exam

This training comprehensively covers all domains of the Certified AIOps Professional exam. The advanced labs and enterprise scenarios mirror the exam's performance-based questions, giving you practical preparation for the most challenging certification in the AIOps track.

Exam voucher included with course enrollment
Advanced performance-based and scenario-driven exam
120-minute exam with 60 questions and 3 lab scenarios
Prerequisite: AIOps Engineer certification or 2+ years experience

View Certification Details

Certified AIOps Professional

Validates your expertise in enterprise-scale AIOps design, multi-cloud monitoring, advanced ML, and operational governance.

60

Questions

120

Minutes

72%

Pass Score

Pricing

Pricing & Registration

Advanced training with multi-cloud labs and comprehensive exam preparation.

AIOps Professional Training

$1,799

Per participant — all-inclusive package

5 days of live instructor-led advanced training
Multi-cloud lab environment (AWS, Azure, GCP)
Official AIOps Professional courseware and lab guides
Certified AIOps Professional exam voucher
Enterprise architecture templates and reference designs
90-day post-training lab access and mentor support

Enroll Now

Group discounts available for 5+ participants

FAQs

Frequently Asked Questions

We recommend the Certified AIOps Engineer certification or at least 2 years of hands-on experience with AIOps tools and practices. You should be comfortable with Python, familiar with at least one cloud platform, and have working knowledge of monitoring and observability tools.

The Engineer course focuses on building individual AIOps components like data pipelines and anomaly detection. The Professional course addresses enterprise-scale challenges: multi-cloud correlation, advanced ML model selection, incident intelligence across hundreds of services, and governance frameworks. It assumes you already know the fundamentals and dives deeper.

No. Experience with at least one cloud platform (AWS, Azure, or GCP) is sufficient. The course teaches the unified approaches and abstraction layers needed to work across all three, and the labs are designed to be accessible regardless of which platform you are most familiar with.

You should understand basic ML concepts (supervised vs. unsupervised learning, model training, and evaluation metrics). The course will teach advanced techniques like deep learning for logs and reinforcement learning for auto-scaling, building on your foundational ML knowledge.

You receive 90 days of post-training access to the multi-cloud lab environment. This extended access allows you to revisit complex scenarios, experiment with different configurations, and thoroughly prepare for the certification exam at your own pace.