Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Powered IT Monitoring for Infrastructure teaches AIOps: using machine learning to keep complex IT systems healthy at a scale humans cannot watch manually. You work with the telemetry that operations teams live on — metrics, logs and traces — and build models for the core AIOps tasks: detecting anomalies, predicting incidents before they cause outages, correlating alerts to cut noise, and assisting root-cause analysis. The course connects these to automated and self-healing responses and the practicalities of running them reliably. You finish able to apply AI to a real infrastructure-monitoring problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AIOps — applying machine learning to IT infrastructure monitoring for anomaly detection, incident prediction, root-cause analysis and automated response.
📋 Course Objectives
1. Work with metrics, logs and trace telemetry.
2. Detect anomalies in infrastructure data.
3. Predict incidents before they cause outages.
4. Correlate alerts and assist root-cause analysis.
5. Design automated and self-healing responses.
👥 Who Should Enroll?
• DevOps, SRE and IT-operations engineers
• Infrastructure and platform teams
• Data scientists in operations
• Students specialising in AIOps
🚀 Key Learning Outcomes
• The ability to apply AIOps to infrastructure.
• An anomaly-detection or incident-prediction project.
• Skills bridging DevOps and machine learning.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Foundations
Apply linear algebra and calculus principles to optimize AI model performance • Develop probabilistic models using Bayesian inference and statistical analysis • Evaluate the trade-offs between different AI architectures, such as CNNs and RNNs
Data Engineering, Preprocessing, and Feature Pipelines
Design data pipelines using Apache Beam and Apache Spark for efficient data processing • Implement data preprocessing techniques, including handling missing values and data normalization • Configure data quality checks using Apache Airflow and Great Expectations
Model Architecture, Algorithm Design, and Methods
Analyze the performance of different machine learning algorithms, such as decision trees and random forests • Develop neural network architectures using TensorFlow and PyTorch for IT monitoring tasks • Optimize model hyperparameters using grid search and random search techniques
Training, Hyperparameter Optimization, and Evaluation
Train AI models using distributed computing frameworks, such as Hadoop and Spark • Evaluate model performance using metrics, such as precision, recall, and F1-score • Implement hyperparameter tuning using Bayesian optimization and gradient-based methods
Deployment, MLOps, and Production Workflows
Deploy AI models using containerization techniques, such as Docker and Kubernetes • Configure model serving pipelines using TensorFlow Serving and AWS SageMaker • Develop monitoring and logging systems using Prometheus and Grafana
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI systems, including bias and fairness • Develop strategies for mitigating bias in AI models, such as data preprocessing and regularization • Evaluate the transparency and explainability of AI models using techniques, such as feature importance and partial dependence plots
Industry Integration, Business Applications, and Case Studies
Apply AI-powered IT monitoring to real-world industry use cases, such as finance and healthcare • Develop business cases for AI adoption, including cost-benefit analysis and ROI calculation • Evaluate the impact of AI on business operations, including process automation and decision-making
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Apache Spark |
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