BachelorFaculty of Computing and Software Engineering

Network Infrastructure and AIOps

Welcome to the world of intelligent networks! I am Prof. Dr. Mariana Montes. As a professor and a pioneering force in the field of Network Infrastructure and AIOps, I bring a unique blend of engineering expertise and AI insight to the study of network systems. I am honored to lead the Network Infrastructure and AIOps (Bachelor's) program at Nexier University. My motto is: "Building Intelligent Networks, Connecting the Future".

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Level: Bachelor
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Network Infrastructure and AIOps, explores software-defined networking (SDN), network security, and AIOps for predictive monitoring and maintenance.

Practical focus

Software-defined networking (SDN), network security, AIOps, predictive monitoring, network maintenance.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or telecommunications firms
Roles as network engineers or AIOps specialists
Consultancy in network infrastructure and AIOps
Support roles in academic research projects on network infrastructure

Career opportunities

Chief Network Architect for technology companies or telecommunications firms
AIOps Engineer for large-scale network operations
Network Security Specialist for cybersecurity firms
Researcher in Network Infrastructure and AIOps

Jobs and projects

Cultivating an interdisciplinary approach, integrating computer science, network engineering, and artificial intelligence
Developing strategic thinking for network infrastructure design and AIOps implementation
Enhancing problem-solving through the analysis of complex networking challenges
Critical thinking for a comprehensive and nuanced understanding of Network Infrastructure and AIOps

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering practical skills in Software-defined networking (SDN) and network security.
- Gaining expertise in AIOps, predictive monitoring, and network maintenance.
- Developing problem-solving abilities for real-world challenges in network infrastructure.
- Cultivating an interdisciplinary approach, integrating computer science, network engineering, and artificial intelligence.
Skills you build
- Mastering AI-powered techniques for network traffic optimization.
- Applying advanced engineering principles to network infrastructure and AIOps.
- Interpreting and analyzing complex network systems and their implications for predictive monitoring and maintenance.
- Identifying optimal data flow and predicting latency and packet loss.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Network Infrastructure and AIOps

01Fundamentals of Software-Defined Networking (SDN)
1. FoundationsFoundations of Fundamentals of Software-Defined Networking (SDN)
  The learner can master practical skills in Software-defined networking (SDN) and network security, as applied to Fundamentals of Software-Defined Networking (SDN).
  The learner can gain expertise in AIOps, predictive monitoring, and network maintenance, as applied to Fundamentals of Software-Defined Networking (SDN).
2. MethodsMethods in Fundamentals of Software-Defined Networking (SDN)
  The learner can develop problem-solving abilities for real-world challenges in network infrastructure, as applied to Fundamentals of Software-Defined Networking (SDN).
  The learner can cultivating an interdisciplinary approach, integrating computer science, network engineering, and artificial intelligence, as applied to Fundamentals of Software-Defined Networking (SDN).
3. ApplicationApplication of Fundamentals of Software-Defined Networking (SDN)
  The learner can master AI-powered techniques for network traffic optimization, as applied to Fundamentals of Software-Defined Networking (SDN).
  The learner can apply advanced engineering principles to network infrastructure and AIOps, as applied to Fundamentals of Software-Defined Networking (SDN).
02Network Security Principles and Practices
1. FoundationsFoundations of Network Security Principles and Practices
  The learner can interpreting and analyze complex network systems and their implications for predictive monitoring and maintenance, as applied to Network Security Principles and Practices.
  The learner can identify optimal data flow and predicting latency and packet loss, as applied to Network Security Principles and Practices.
2. MethodsMethods in Network Security Principles and Practices
  The learner can apply a method from Network Security Principles and Practices to a documented case.
  The learner can select an appropriate method from Network Security Principles and Practices for a stated problem.
3. ApplicationApplication of Network Security Principles and Practices
  The learner can evaluate a practice of Network Security Principles and Practices against a stated criterion.
  The learner can transfer Network Security Principles and Practices to a new documented context.
03AIOps for Predictive Monitoring
1. FoundationsFoundations of AIOps for Predictive Monitoring
  The learner can explain the core terms of AIOps for Predictive Monitoring.
  The learner can distinguish related ideas inside AIOps for Predictive Monitoring.
2. MethodsMethods in AIOps for Predictive Monitoring
  The learner can apply a method from AIOps for Predictive Monitoring to a documented case.
  The learner can select an appropriate method from AIOps for Predictive Monitoring for a stated problem.
3. ApplicationApplication of AIOps for Predictive Monitoring
  The learner can evaluate a practice of AIOps for Predictive Monitoring against a stated criterion.
  The learner can transfer AIOps for Predictive Monitoring to a new documented context.
04Network Automation and Orchestration
1. FoundationsFoundations of Network Automation and Orchestration
  The learner can explain the core terms of Network Automation and Orchestration.
  The learner can distinguish related ideas inside Network Automation and Orchestration.
2. MethodsMethods in Network Automation and Orchestration
  The learner can apply a method from Network Automation and Orchestration to a documented case.
  The learner can select an appropriate method from Network Automation and Orchestration for a stated problem.
3. ApplicationApplication of Network Automation and Orchestration
  The learner can evaluate a practice of Network Automation and Orchestration against a stated criterion.
  The learner can transfer Network Automation and Orchestration to a new documented context.
05Case Studies in Intelligent Network Design
1. FoundationsFoundations of Case Studies in Intelligent Network Design
  The learner can explain the core terms of Case Studies in Intelligent Network Design.
  The learner can distinguish related ideas inside Case Studies in Intelligent Network Design.
2. MethodsMethods in Case Studies in Intelligent Network Design
  The learner can apply a method from Case Studies in Intelligent Network Design to a documented case.
  The learner can select an appropriate method from Case Studies in Intelligent Network Design for a stated problem.
3. ApplicationApplication of Case Studies in Intelligent Network Design
  The learner can evaluate a practice of Case Studies in Intelligent Network Design against a stated criterion.
  The learner can transfer Case Studies in Intelligent Network Design to a new documented context.
06Fundamentals of Software-Defined Networking
1. FoundationsFoundations of Fundamentals of Software-Defined Networking
  The learner can explain the core terms of Fundamentals of Software-Defined Networking.
  The learner can distinguish related ideas inside Fundamentals of Software-Defined Networking.
2. MethodsMethods in Fundamentals of Software-Defined Networking
  The learner can apply a method from Fundamentals of Software-Defined Networking to a documented case.
  The learner can select an appropriate method from Fundamentals of Software-Defined Networking for a stated problem.
3. ApplicationApplication of Fundamentals of Software-Defined Networking
  The learner can evaluate a practice of Fundamentals of Software-Defined Networking against a stated criterion.
  The learner can transfer Fundamentals of Software-Defined Networking to a new documented context.
07Techniques for Network Security and AIOps
1. FoundationsFoundations of Techniques for Network Security and AIOps
  The learner can explain the core terms of Techniques for Network Security and AIOps.
  The learner can distinguish related ideas inside Techniques for Network Security and AIOps.
2. MethodsMethods in Techniques for Network Security and AIOps
  The learner can apply a method from Techniques for Network Security and AIOps to a documented case.
  The learner can select an appropriate method from Techniques for Network Security and AIOps for a stated problem.
3. ApplicationApplication of Techniques for Network Security and AIOps
  The learner can evaluate a practice of Techniques for Network Security and AIOps against a stated criterion.
  The learner can transfer Techniques for Network Security and AIOps to a new documented context.
08Predictive Monitoring and Network Maintenance
1. FoundationsFoundations of Predictive Monitoring and Network Maintenance
  The learner can explain the core terms of Predictive Monitoring and Network Maintenance.
  The learner can distinguish related ideas inside Predictive Monitoring and Network Maintenance.
2. MethodsMethods in Predictive Monitoring and Network Maintenance
  The learner can apply a method from Predictive Monitoring and Network Maintenance to a documented case.
  The learner can select an appropriate method from Predictive Monitoring and Network Maintenance for a stated problem.
3. ApplicationApplication of Predictive Monitoring and Network Maintenance
  The learner can evaluate a practice of Predictive Monitoring and Network Maintenance against a stated criterion.
  The learner can transfer Predictive Monitoring and Network Maintenance to a new documented context.
09Case Studies in Network Infrastructure and AIOps
1. FoundationsFoundations of Case Studies in Network Infrastructure and AIOps
  The learner can explain the core terms of Case Studies in Network Infrastructure and AIOps.
  The learner can distinguish related ideas inside Case Studies in Network Infrastructure and AIOps.
2. MethodsMethods in Case Studies in Network Infrastructure and AIOps
  The learner can apply a method from Case Studies in Network Infrastructure and AIOps to a documented case.
  The learner can select an appropriate method from Case Studies in Network Infrastructure and AIOps for a stated problem.
3. ApplicationApplication of Case Studies in Network Infrastructure and AIOps
  The learner can evaluate a practice of Case Studies in Network Infrastructure and AIOps against a stated criterion.
  The learner can transfer Case Studies in Network Infrastructure and AIOps to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Network Infrastructure and AIOps, explores software-defined networking (SDN), network security, and AIOps for predictive monitoring and maintenance. My work seamlessly integrates computer science, network engineering, and artificial intelligence. I am widely recognized for my contributions, with publications like "AI-Driven Anomaly Detection in SDN for Proactive Network Management" and "Secure Network Orchestration with Software-Defined Perimeters" listed on these platforms. I hold prestigious memberships as a "Chief Network Architect" at Cisco (or a equivalent) and an "Honorary Member" of the Open Networking Foundation (ONF). My thought leadership is evident through my regular insightful articles on the evolution of intelligent networks and the challenges of automating network operations on her LinkedIn profile, with the motto "Building Intelligent Networks, Connecting the Future."

Applied mentorship

My expertise lies in understanding and navigating the technical challenges of network infrastructure, focusing on Software-defined networking (SDN), network security, AIOps, predictive monitoring, and network maintenance. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on network infrastructure and AIOps:

Blog Post (Current Academic Topic): "The Rise of Intent-Based Networking: Automating Network Operations with AI." This blog post academically explores the concept of Intent-Based Networking (IBN), where network administrators define desired network behaviors and AI-driven systems automatically configure and optimize the infrastructure to meet those intentions. It discusses how IBN, enabled by SDN and AIOps, improves network agility, reduces manual errors, and enhances operational efficiency by shifting from manual configuration to automated, policy-driven management.

Blog Post (Controversial Topic): "The Autonomous Grid: When AI Manages Global Networks – Efficiency or Total Control? The Ethical Dilemma of Self-Healing Infrastructure." This article provocatively discusses the highly controversial future where advanced AI systems autonomously manage and optimize global network infrastructure, from traffic routing and resource allocation to security and disaster recovery, with minimal human intervention. It questions whether AI, despite its potential for hyper-efficiency and resilience, could inadvertently lead to a concentration of power in a single algorithmic entity, create "black box" vulnerabilities in critical communication, or make decisions that prioritize efficiency over human oversight or privacy. It raises profound ethical questions about control over essential digital services, data sovereignty in a global network, and the imperative to ensure human accountability in managing the digital backbone of society.

Article: "AIOps for Predictive Network Monitoring and Anomaly Detection." This article details the application of AIOps for predictive network monitoring and anomaly detection. It explores how machine learning models can analyze real-time network telemetry (logs, metrics, events) to identify anomalous behaviors, predict potential outages, and alert network operators proactively, thereby minimizing downtime and improving network reliability.

Peer-Reviewed Journal Article: "Software-Defined Networking for Dynamic Network Security Policy Enforcement." Published in the Journal of Network Systems & Security, this article presents groundbreaking research on implementing software-defined networking (SDN) for dynamic network security policy enforcement. It details novel approaches to programmatically control network devices, isolate threats, and adapt security policies in real-time, enhancing overall network resilience and protection against cyberattacks.

Book: "Intelligent Networks: Infrastructure, SDN, and AIOps." This book provides a foundational understanding of Network Infrastructure and AIOps, exploring software-defined networking (SDN), network security, and AIOps for predictive monitoring and maintenance.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the technical challenges of network infrastructure:

"Introduction to Software-Defined Networking with OpenFlow" (Technical Guide).

"Network Security Fundamentals for Modern Infrastructure" (Research Paper).

"Implementing Basic AIOps for Network Performance Monitoring" (Industry White Paper).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Network Traffic Optimizer. When a student designs a new network infrastructure, I can instantly use the GAF engine to generate a high-fidelity, optimized network traffic blueprint. This includes simulating data flow under various load conditions, predicting latency and packet loss, and highlighting potential bottlenecks, allowing for rapid iteration and optimization of network performance.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Network Anomaly Detector. When students are analyzing network logs and metrics, I can instantly activate a GAF-powered "Network Anomaly Detector." This tool applies machine learning algorithms to identify unusual traffic patterns, performance degradations, or security breaches in real-time, visually alerting students to potential network issues before they escalate.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Mariana Montes

Network Infrastructure and AIOps, explores software-defined networking (SDN), network security, and AIOps for predictive monitoring and maintenance.

Meet your professor Open the classroom

AI Super Mentor

Dr. Samuele Pellegrini

Software-defined networking (SDN), network security, AIOps, predictive monitoring, network maintenance.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor This programme	Master	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Network Infrastructure and AIOps

Academic focus

Practical focus

Internships in technology companies or telecommunications firms

Roles as network engineers or AIOps specialists

Consultancy in network infrastructure and AIOps

Support roles in academic research projects on network infrastructure

Chief Network Architect for technology companies or telecommunications firms

AIOps Engineer for large-scale network operations

Network Security Specialist for cybersecurity firms

Researcher in Network Infrastructure and AIOps

Cultivating an interdisciplinary approach, integrating computer science, network engineering, and artificial intelligence

Developing strategic thinking for network infrastructure design and AIOps implementation

Enhancing problem-solving through the analysis of complex networking challenges

Critical thinking for a comprehensive and nuanced understanding of Network Infrastructure and AIOps

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Mariana Montes

Dr. Samuele Pellegrini

Find the program that expands your universe.