Introduction to the Course

Course Objectives

• Understand the fundamentals of AI-powered IT monitoring and predictive analytics.

• Learn how machine learning models are applied to infrastructure data such as logs, metrics, and traces.

• Explore anomaly detection, forecasting, and root-cause analysis techniques for IT systems.

• Gain hands-on experience designing predictive monitoring workflows for real-world IT environments.

• Understand ethical, scalability, and data governance considerations in AI-based monitoring systems.

What Will You Learn (Modules)

Module 1: Architecture & Instrumentation of AI-Powered Monitoring Systems

• Introduction to AIOps (AI in IT Operations)
• Traditional vs predictive monitoring approaches
• Key components of IT infrastructure (cloud, on-prem, hybrid)
• Data sources: Logs, metrics, events, traces
• The importance of real-time observability

Module 2: AI-Driven Analytics: Time Series Forecasting & Anomaly Detection

• Data preprocessing for monitoring (time windows, lag features, trends)
• Predictive analytics techniques (ARIMA, Facebook Prophet, LSTM)
• Anomaly detection (Z-score, Isolation Forest, Autoencoders)
• Evaluation metrics (MAE, RMSE, Precision/Recall)

Module 3: Automation, Alerting, and Scalable AI Monitoring Pipelines

• Intelligent alerting systems (threshold vs behavior-based alerts)
• Noise reduction via event correlation & suppression
• Automation strategies (auto-remediation & self-healing systems)
• AIOps workflow design (full-stack)

Who Should Take This Course?

This course is ideal for:

• IT & Infrastructure Engineers: Professionals managing servers, networks, and cloud infrastructure who want to move from reactive to predictive monitoring.

• DevOps & Site Reliability Engineers (SREs): Individuals focused on improving system reliability, automation, and incident response using AI-driven insights.

• Data & AI Professionals: Those interested in applying machine learning techniques to operational and infrastructure-level data.

• Cybersecurity & IT Operations Professionals: Experts seeking early threat detection, anomaly identification, and improved system resilience.

• Students & Career Switchers: Learners with backgrounds in IT, computer science, cloud computing, or data analytics aiming to enter the AIOps domain.

Job Oppurtunities

After completing this course, learners will be prepared for roles such as:

• AIOps Engineer: Building and managing AI-driven monitoring and automation systems.

• Site Reliability Engineer (SRE): Using predictive analytics to improve uptime and system resilience.

• IT Operations Analyst: Leveraging AI insights for performance optimization and incident prevention.

• Cloud Monitoring Specialist: Managing AI-powered monitoring solutions for cloud and hybrid environments.

• DevOps Engineer: Integrating predictive monitoring into CI/CD and automation pipelines.

Why Learn With Nanoschool?

At Nanoschool, you gain industry-focused training designed for real-world IT challenges. Key benefits include:

• Expert-Led Instruction: Learn from professionals experienced in AI, cloud infrastructure, and IT operations.

• Hands-On Learning: Work with real infrastructure datasets, monitoring tools, and predictive models.

• Industry-Aligned Curriculum: Stay current with modern AIOps platforms and enterprise monitoring practices.

• Career Support: Resume guidance, interview preparation, and job transition assistance in IT and AI roles.

Key outcomes of the course

By the end of this course, you will:

• Design and implement AI-powered monitoring strategies for IT infrastructure.

• Predict system failures and performance issues before they impact users.

• Apply machine learning models to logs, metrics, and operational data.

• Understand governance, security, and ethical considerations in AI-driven IT systems.

• Be job-ready for roles in AIOps, cloud monitoring, DevOps, and IT reliability engineering.

Step into the future of IT operations and discover how AI-powered predictive analytics is reshaping infrastructure monitoring. Learn to build smarter, more resilient systems that minimize downtime, reduce costs, and enable proactive decision-making.