Diploma in AI Security at the Edge and Lightweight MLOps
About us Diploma in AI Security at the Edge and Lightweight MLOps
The Diploma in AI Security at the Edge and Lightweight MLOps focuses on the secure and efficient development and deployment of Artificial Intelligence (AI) models in edge environments, and on optimizing Machine Learning (MLOps) workflows with a “lightweight” approach. The diploma covers key topics such as AI model security, edge data protection, optimization of limited computational resources, and the implementation of efficient MLOps pipelines. It aims to ensure the confidentiality, integrity, and availability of AI solutions at the edge, using tools and practices to mitigate vulnerabilities and improve scalability. The program aims to create a solid foundation for the secure implementation of AI in practice. The program offers hands-on training with a focus on security tools, edge AI deployment platforms, and model monitoring and management techniques. It promotes the development of skills in creating efficient and secure models for use in applications such as industrial automation, autonomous vehicles, and IoT devices. Students prepare for roles such as AI security engineers, edge data scientists, and MLOps architects, enhancing their career prospects in the growing demand for these areas.
Diploma in AI Security at the Edge and Lightweight MLOps
- Format: Hybrid
- Duration: 6–8 months
- Hours: 800 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Strat date: 03-12-2026
- Available places: 2
1,550 $
Competencies and results
What you will learn
1. Mastering AI Security at the Edge and Lightweight MLOps: Design, Implementation, and Optimization [The text abruptly shifts to a seemingly unrelated topic:] [The text abruptly shifts again ...
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Building robust and efficient AI models for edge environments.
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Integrating AI into edge systems using optimized hardware and software architectures.
Implementing AI security strategies to protect data and models on edge devices.
Deploying and managing machine learning (ML) models using lightweight MLOps techniques.
Optimizing AI models to maximize performance and energy efficiency on edge devices.
Designing and implementing data pipelines for data ingestion, processing, and preparation for model training.
Selecting and using lightweight MLOps frameworks and tools for model lifecycle management.
Applying continuous monitoring and evaluation techniques to models in production.
Analyzing and mitigating security risks throughout the AI model lifecycle.
Evaluating the ethical and privacy implications of deploying AI at the edge.
2. Master Implementation of AI Security at the Edge and Lightweight MLOps: Strategies, Development, and Superior Performance
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Understand the fundamentals of Artificial Intelligence (AI) for edge security.
Master strategies for the effective implementation of AI on edge devices.
Develop Machine Learning (ML) models optimized for the edge, considering resource constraints.
Apply lightweight MLops methodologies for model lifecycle management at the edge.
Optimize the performance of AI/ML models for real-time inference at the edge.
Explore distributed AI and ML architectures for edge security.
Ensure the security and privacy of data at the edge.
Implement cybersecurity threat detection and response mechanisms at the edge.
Manage and monitor the deployment and performance of AI/ML models in edge environments.
Study practical use cases of AI in security across different industries.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Master's Degree in AI Security at the Edge and Lightweight MLOps: Comprehensive Design, Development, and Optimization
Here is the content you will learn in the Master’s in Edge AI Security and Lightweight MLOps: Comprehensive Design, Development, and Optimization:
1. **AI at the Edge Fundamentals:**
* Understand edge AI architecture: devices, platforms, and networks.
* Explore edge limitations: computational resources, memory, power, and connectivity.
* Analyze edge-specific security challenges: vulnerabilities, threats, and attacks.
2. **Lightweight MLOps for the Edge:**
* Master MLOps practices adapted to the edge environment: automation, CI/CD, and monitoring.
* Implement edge-optimized data and training pipelines.
* Manage edge models: deployment, updates, versioning, and rollback.
3. **Design and Development of Secure Edge AI Systems:**
* Design secure and resilient AI architectures for the edge. * Develop lightweight and optimized AI models for edge devices.
* Integrate security techniques into the AI lifecycle: encryption, authentication, access control.
4. **AI Model Optimization for the Edge:**
* Apply model compression techniques: pruning, quantization, distillation.
* Optimize model performance on different edge platforms: CPU, GPU, TPU.
* Use optimization tools and frameworks: TensorFlow Lite, ONNX Runtime, etc.
5. **Data Security and Privacy at the Edge:**
* Implement data protection techniques: encryption, anonymization, aggregation.
* Ensure data privacy: differential privacy, federated learning.
* Comply with privacy regulations: GDPR, CCPA, etc.
6. **Edge Security Testing and Assessment:**
* Conduct penetration testing and vulnerability analysis on edge AI systems.
* Evaluate the robustness and reliability of AI models.
* Monitor and detect anomalies in real time.
7. **Secure Edge AI Deployment:**
* Deploy secure AI models on edge devices: smartphones, IoT, autonomous vehicles.
* Integrate AI into various industries: healthcare, manufacturing, retail, etc.
* Manage the lifecycle of edge AI systems.
5. Excellence in AI Security at the Edge and Lightweight MLOps: Architecture, Deployment, and Scalability [The text abruptly shifts to a seemingly unrelated topic:] [The text abruptly shifts again:] ...
5. **Excellence in Edge AI Security and Lightweight MLOps: Architecture, Deployment, and Scalability**
- Understand the architecture of edge AI systems for security.
- Learn to implement and optimize lightweight Machine Learning (ML) models for deployment on resource-constrained devices.
- Master Machine Learning Operations (MLOps) strategies for managing the lifecycle of edge models, including automated deployment, monitoring, and updates.
- Explore edge AI-specific security techniques, such as protection against adversarial attacks and preserving data privacy.
- Analyze scalability considerations in the context of edge AI, including managing multiple devices and coordinating inference.
- Become familiar with the key tools and technologies for developing, deploying, and Manage secure and scalable AI solutions at the edge.
Apply design and implementation methodologies for secure AI systems, considering security from the initial development phase.
Evaluate the performance and efficiency of AI models at the edge, using metrics relevant to security and optimization.
Learn to manage the data lifecycle at the edge, including collection, processing, and secure storage.
Study practical use cases of AI at the edge for security, such as intrusion detection, surveillance, and video analytics.
6. Specialization in AI Security at the Edge and Lightweight MLOps: Development, Implementation, and Continuous Improvement
6. Specialization in AI Security at the Edge and Lightweight MLOps: Development, Implementation, and Continuous Improvement
- Understand the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML), with a focus on their application at the edge.
- Master the concepts of AI security, including the identification and mitigation of vulnerabilities in AI models, both during training and deployment at the edge.
- Learn to design and implement secure AI solutions for edge devices, considering resource constraints and privacy and security requirements.
- Explore Lightweight MLOps tools and techniques for managing the lifecycle of AI models at the edge, from development to deployment and maintenance.
- Apply Continuous improvement methodologies to optimize the performance and security of AI models at the edge, including monitoring, evaluation, and updating.
Become familiar with the most suitable hardware architectures and software platforms for implementing AI at the edge.
Analyze practical use cases and develop projects to apply the acquired knowledge in real-world scenarios, such as IoT device security, machine vision in industrial environments, and healthcare system monitoring.
Learn to evaluate the performance and energy efficiency of AI solutions at the edge, optimizing the use of limited resources.
Acquire skills in effectively communicating results and findings related to AI security at the edge to both technical and non-technical audiences.
Understand the implications Ethical and regulatory considerations of AI use at the edge, including data privacy, transparency, and accountability.
Who this program is for:
Diploma in AI Security at the Edge and Lightweight MLOps
- Professionals and graduates with experience or interest in **Artificial Intelligence (AI)**, **Machine Learning (ML)**, and **Machine Learning Operations (MLOps)**.
- Software Engineers, **Data Scientists**, **Data Engineers**, and **AI Architects** looking to specialize in AI security in *Edge* environments.
- Information Security and **Cybersecurity** experts who wish to expand their knowledge in protecting AI and ML systems.
- Professionals involved in the development, implementation, and management of AI projects in sectors such as **Industry 4.0**, **IoT**, **Telecommunications**, **Finance**, and **Healthcare**.
- Individuals interested in **lightweight MLOps** and **deploying AI models at the edge**.
Seeking optimization and efficiency.
Recommended Requirements: Basic programming knowledge (Python), AI and ML fundamentals, and experience in cloud and/or edge environments.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
**Module 1 — Mastering Edge AI Security and Lightweight MLOps: Design, Implementation, and Optimization**
1.1 Edge AI and MLOps Fundamentals: Key Concepts
1.2 Designing Secure Edge Architectures: Principles
1.3 Implementing Edge Security Measures: Techniques
1.4 Optimizing Edge AI Performance: Strategies
1.5 Lightweight MLOps: Introduction and Workflows
1.6 Ensuring Data Integrity at the Edge: Methods
1.7 Protecting Against Edge Threats: Attacks and Defenses
1.8 Edge Security Tools and Technologies: Selection
1.9 Edge Anomaly Monitoring and Detection: Implementation
1.10 Case Studies: Practical Examples and Lessons Learned
1.10
2.2 AI Security Fundamentals at the Edge: Key Concepts
2.2 Designing Secure Edge Architectures
2.3 Implementing Access Controls and Authentication
2.4 Data Protection and Privacy at the Edge
2.5 Security in the AI Model Lifecycle
2.6 Lightweight MLOps Strategies for the Edge
2.7 Real-Time Threat Monitoring and Detection
2.8 Security Testing and Vulnerability Assessment
2.9 Integrating Secure AI into Edge Devices
2.20 Case Studies: Successful Implementations and Challenges
2.3
3.3 Fundamentals of AI Security at the Edge and Lightweight MLOps
3.2 Designing Secure AI Systems at the Edge
3.3 Practical Implementation of Lightweight MLOps for AI
3.4 Optimizing Performance and Security at the Edge
3.5 Deep Learning for AI Security
3.6 Implementing Deep Learning Models at the Edge
3.7 Advanced Optimization Strategies for Lightweight MLOps
3.8 Testing and Validating AI Security Systems
3.9 Risk and Vulnerability Management in AI Systems
3.30 Case Studies: AI Security Implementation and Deployment
3.4
4.4 AI Security Fundamentals at the Edge and MLOps: Key Concepts
4.2 Designing Secure Architectures for AI at the Edge
4.3 Implementing Lightweight MLOps for Secure Deployment
4.4 Deep Learning Techniques for Edge Security
4.5 Performance Optimization in Constrained Environments
4.6 AI Security Risk Assessment and Mitigation
4.7 Data Protection and Privacy Strategies
4.8 AI Security Incident Monitoring and Management
4.9 Developing Security Policies and Procedures
4.40 AI Security Auditing and Continuous Improvement
5.5 AI Security Fundamentals at the Edge: Concepts and Principles
5.5 Designing Secure Architectures for AI on Edge Devices
5.3 Specific Threats and Vulnerabilities at the Edge
5.4 Data Protection Strategies in Edge Environments
5.5 Designing Attack-Resistant AI Models
5.6 Risk Assessment and Mitigation in Edge AI Security
5.7 Tools and Technologies for Edge AI Security
5.8 Designing Intrusion Detection and Prevention Systems for Edge AI
5.5 Implementing AI Models at the Edge: Techniques and Challenges
5.5 Optimizing Models for Edge Devices: Performance and Efficiency
5.3 Integrating Lightweight MLOps into the Edge AI Workflow
5.4 Securely Deploying AI Models in Edge Environments
5.5 Monitoring and Managing the Lifecycle of Edge Models
5.6 Testing and Validating AI Implementations at the Edge
5.7 Implementing Model Update and Rollback Mechanisms
5.8 Developing AI Applications at the Edge: Practical Cases
3.5 Optimizing the Performance of AI Models for the Edge
3.5 Lightweight MLOps Techniques for Model Lifecycle Management
3.3 Implementing Security Measures for Data Protection
3.4 Monitoring and Anomaly Detection Strategies in AI Systems
3.5 Optimizing Resource Consumption in Edge Environments
3.6 Automating Model Deployment and Updates
3.7 Implementing Feedback and Continuous Improvement Mechanisms
3.8 Optimizing System Scalability and Adaptability
4.5 Designing End-to-End AI Systems: From Design to Implementation
4.5 Developing AI Models with an Emphasis on Security
4.3 Implementation of Comprehensive Security Strategies
4.4 Integration of MLOps for the Complete AI Lifecycle
4.5 Design of Scalable and Fault-Resistant Architectures
4.6 Strategies for Monitoring and Managing Production Models
4.7 Evaluation and Continuous Improvement of Security and Performance
4.8 Design of AI Systems for Specific Use Cases
5.5 Architecture of AI Systems at the Edge: Design and Components
5.5 Deployment of AI Models in Edge Environments: Strategies and Techniques
5.3 Horizontal and Vertical Scalability in Edge AI Systems
5.4 Implementation of Fault Recovery Mechanisms
5.5 Design of Distributed Systems for AI at the Edge
5.6 Optimization of Performance and Energy Efficiency
5.7 Security Considerations in Deployment and Scalability
5.8 Implementation of MLOps Platforms for Management Systems
6.5 Secure Development of AI Models: Best Practices
6.5 Implementing Security Measures During Development
6.3 Integrating MLOps for Continuous Implementation
6.4 Threat Detection and Monitoring Strategies
6.5 Security Management Throughout the Model Lifecycle
6.6 Continuous Improvement of AI Performance and Security
6.7 Implementing Feedback and Learning Mechanisms
6.8 Developing Compliant AI Systems
7.5 Advanced Architecture of Edge AI Systems
7.5 Designing Security Solutions for Edge AI
7.3 Implementing Distributed and Scalable Architectures
7.4 Applying Model Optimization Techniques
7.5 Designing Incident Detection and Response Systems
7.6 Integrating MLOps for Comprehensive Lifecycle Management
7.7 Developing AI Applications for Use Cases Specifics
7.8 Implementation of Continuous Improvement Strategies
8.5 Vulnerability Analysis in Edge AI Systems
8.5 Application of Risk Analysis Techniques
8.3 Threat Analysis and Countermeasure Design
8.4 Implementation of Risk Mitigation Strategies
8.5 Performance Analysis and Model Optimization
8.6 Application of MLOps for Continuous Improvement
8.7 Analysis of Use Cases and Practical Applications
8.8 Analysis of System Scalability and Adaptability
6.6 Continuous Improvement Strategies in AI Security and MLOps
6.2 Edge Vulnerability and Threat Assessment
6.3 Implementation of Penetration Testing and Red Teams
6.4 Performance Optimization in AI Systems
6.5 Log Monitoring and Analysis for Anomaly Detection
6.6 Implementation of Incident Response Mechanisms
6.7 Automation of Continuous Security Improvement
6.8 Development of Security Dashboards and Reports
6.9 Integration of Feedback into the Process
6.60 Implementation of a Robust AI Security Lifecycle
6.7
7. AI Security Mastery at the Edge and Lightweight MLOps: Design, Implementation, and Optimization
7.7 Fundamental Concepts of AI Security at the Edge: Threats, Vulnerabilities, and Attacks
7.2 Designing Secure Architectures for Edge Devices
7.3 Implementing Access Controls and Authentication
7.4 Encryption and Data Protection Techniques at the Edge
7.7 Optimizing AI Models for Performance and Security
7.6 Integrating Lightweight MLOps for Secure Deployment and Model Management
7.7 Monitoring and Anomaly Detection in Edge Environments
7.8 Security Testing for AI Models
7.9 Compliance and Privacy Considerations
7.70 Case Study: Designing and Optimizing an AI Security System at the Edge
2. AI Edge Security Master Implementation and Lightweight MLOps: Strategies, Development, and Superior Performance
2.7 AI Edge Security Implementation Strategies: Hardware and Software Selection
2.2 Developing Robust AI Models Resistant to Adversarial Attacks
2.3 Implementing Firewalls and Intrusion Detection Systems at the Edge
2.4 Designing Secure Data Pipelines
2.7 Optimizing AI Model Performance for Resource-Constrained Devices
2.6 Developing MLOps Pipelines for Deployment and Continuous Updating
2.7 Advanced Monitoring and Log Analysis
2.8 Penetration Testing and Vulnerability Assessment
2.9 Incident Response Strategies 2.70 Case Study: Implementation and Optimization of a High-Performance AI Security System
3. AI Security at the Edge and Lightweight MLOps: Deep Learning, Implementation, and Advanced Optimization
3.7 Deep Learning for Edge Security: Advanced Models and Architectures
3.2 Implementation of Defense Techniques Against Adversarial Attacks
3.3 Optimization of Deep Learning Models for Edge Devices
3.4 Implementation of Deep Learning-Based Threat Detection Systems
3.7 Design of MLOps Pipelines for Deep Learning
3.6 Data Quality Management and Model Performance
3.7 Implementation of Intelligent Monitoring Systems
3.8 Security Auditing and Regulatory Compliance
3.9 Development of Disaster Recovery Strategies 3.70 Case Study: Implementation and Optimization of an Advanced AI Security System
4. Master’s in Edge AI Security and Lightweight MLOps: Comprehensive Design, Development, and Optimization
4.7 Comprehensive Design of Edge AI Security Systems: Complex Architectures
4.2 Development of Secure and Resistant AI Models Against Sophisticated Attacks
4.3 Implementation of Multi-Layer Security Controls
4.4 Design of Secure and Scalable Data Pipelines
4.7 Optimization of AI Model Performance and Energy Efficiency
4.6 Implementation of Complete MLOps Pipelines
4.7 Real-Time Security Monitoring and Management
4.8 Implementation of Automated Security Testing
4.9 Regulatory Compliance and Risk Management 4.70 Case Study: Design, Development, and Optimization of a Comprehensive AI Security System
7. AI Security Excellence at the Edge and Lightweight MLOps: Architecture, Deployment, and Scalability
7.7 Architecture of Scalable AI Security Systems at the Edge
7.2 Deployment of AI Models in Distributed Environments
7.3 Performance and Latency Optimization
7.4 Implementation of Horizontal and Vertical Scalability Strategies
7.7 Design of Scalable MLOps Pipelines
7.6 Deployment and Update Automation
7.7 System Health and Performance Monitoring
7.8 Security Management in High Availability Environments
7.9 Cost and Efficiency Considerations
7.70 Case Study: Architecture, Deployment, and Scalability of an AI Security System of Excellence
6. Specialization in Edge AI Security and Lightweight MLOps: Development, Implementation, and Continuous Improvement
6.7 Development of AI Models Specific to Edge Security
6.2 Implementation of Advanced Defense Techniques
6.3 Continuous Optimization of Performance and Security
6.4 Design of MLOps Pipelines for Continuous Improvement
6.7 Implementation of a Feedback System
6.6 Data Monitoring and Analysis for Model Improvement
6.7 Regular Security Testing and Updates
6.8 Configuration Management and Version Control
6.9 Strategies for Adapting to New Threats
6.70 Case Study: Development, Implementation, and Continuous Improvement of a Specialized AI Security System
7. Architecture and Expert Application of AI Security at the Edge and Lightweight MLOps
7.7 Designing Advanced AI Security Architectures at the Edge: Component Integration
7.2 Applying Defense Techniques Against Sophisticated Attacks
7.3 Implementing Behavior-Based Threat Detection Systems
7.4 Designing Secure and Efficient Data Pipelines
7.7 Optimizing Performance for Different Types of Edge Devices
7.6 Implementing MLOps Pipelines for Deploying and Managing Complex Models
7.7 Real-Time Data Monitoring and Analysis
7.8 Integration with Incident Management Systems
7.9 Cost and Scalability Considerations
7.70 Case Study: Designing and Implementing an Expert AI Security System
8. Advanced Analysis and Application of AI Security at the Edge and Lightweight MLOps
8.7 In-depth Analysis of Threats and Vulnerabilities in Edge Environments
8.2 Application of AI Techniques for Attack Detection and Prevention
8.3 Implementation of Advanced Countermeasures
8.4 Data Analysis and Performance Evaluation
8.7 Performance Optimization and Energy Efficiency
8.6 Implementation of MLOps Systems for Automated Deployment
8.7 Real-Time Monitoring and Incident Analysis
8.8 Continuous Security Assessment and Improvement
8.9 Privacy and Compliance Considerations
8.70 Case Study: Advanced Analysis and Application of an AI Security System
8.8 Key Concepts and Current Landscape of AI Security at the Edge
8.8 Specific Threats and Vulnerabilities in Edge Environments
8.3 In-Depth Analysis of Attacks and Exploitation Techniques in AI Systems
8.4 Implementation of Advanced Defense and Mitigation Strategies
8.5 Monitoring and Anomaly Detection with Machine Learning
8.6 Application of Forensic Analysis Techniques on Edge Devices
8.7 Performance and Security Optimization in Constrained Environments
8.8 Integrating AI Security with Lightweight MLOps
8.8 Case Studies: Analysis and Practical Application in Different Sectors
8.80 The Future of AI Security at the Edge: Trends and Challenges
9.9 Introduction to Edge AI Security and Lightweight MLOps
9.9 Designing Edge AI Security Systems
9.3 Optimizing AI Models for Constrained Environments
9.4 Implementing Lightweight MLOps for AI Security
9.5 Tools and Technologies for Edge AI Security
9.6 Case Studies: Design and Optimization
9.9 AI Security Implementation Strategies
9.9 Developing Edge AI Models
9.3 Implementation Performance and Efficiency
9.4 Integration with MLOps Platforms
9.5 AI Security Monitoring and Management
9.6 Case Studies: Strategic Implementation
3.9 Fundamentals of Deep Learning in AI Security
3.9 Implementing Deep Learning Models at the Edge
3.3 Advanced Optimization of Models for AI Security
3.4 Learning Transfer Techniques
3.5 Evaluation and Validation of Models
3.6 Advanced Applications and Case Studies
4.9 Designing Large-Scale AI Security Systems
4.9 Developing Comprehensive AI Solutions
4.3 Comprehensive Optimization of AI Security Systems
4.4 Managing the Model Lifecycle
4.5 Integrating Advanced Tools and Technologies
4.6 Practical Projects and Case Studies
5.9 Scalable AI Security System Architecture
5.9 Deploying Models in Distributed Environments
5.3 Horizontal and Vertical Scalability
5.4 Performance Monitoring and Management
5.5 Designing Fault-Tolerant Systems
5.6 Success Stories and Best Practices
6.9 Developing a Continuous Improvement Process
6.9 Implementing Continuous Testing and Evaluation
6.3 Updating and Retraining Models
6.4 Version Management and Quality Control
6.5 Automating the Improvement Process
6.6 Implementing A Feedback Loop System
7.9 Architecture of Complex AI Security Systems
7.9 Integration of Multiple Technologies
7.3 Interface and API Design
7.4 Microservices Implementation
7.5 Security and Cybersecurity in AI Systems
7.6 Complex Architecture Projects
8.9 Vulnerability Analysis in AI Systems
8.9 Application of Advanced Defense Techniques
8.3 Attack Detection and Mitigation
8.4 Risk and Threat Analysis
8.5 Regulatory and Ethical Compliance
8.6 Case Studies: Analysis and Application
8.9
1. Mastering Edge AI Security and Lightweight MLOps: Design, Implementation, and Optimization
1.1 Edge AI Fundamentals: Architecture and Security Challenges
1.2 Designing Lightweight MLOps Systems: Pipelines and Model Management
1.3 Secure Edge Model Deployment: Protection Techniques
1.4 Optimizing Performance and Energy Efficiency on Edge Devices
1.5 Model Testing and Validation: Ensuring Integrity and Reliability
1.6 Tools and Frameworks for Edge Security and MLOps
1.7 Case Studies: Practical Implementation Examples
1.8 Best Practices for Security Throughout the Model Lifecycle
1.9 Security Incident Monitoring and Response
1.10 Final Project: Design and Implementation of a Secure Edge System
2. AI Security Master Implementation at the Edge and Lightweight MLOps: Strategies, Development, and Superior Performance
2.1 Advanced security strategies for AI models at the edge.
2.2 Development of scalable and resilient MLOps pipelines.
2.3 Performance optimization using hardware acceleration techniques.
2.4 Implementation of encryption and authentication for data protection.
2.5 Vulnerability analysis and mitigation techniques at the edge.
2.6 Continuous integration/continuous deployment (CI/CD) for secure MLOps.
2.7 Proactive monitoring and real-time anomaly detection.
2.8 Version management and change control in AI models.
2.9 Privacy and regulatory compliance considerations.
2.10 Final project: Implementation of a high-performance and secure MLOps system.
3. AI Security at the Edge and Lightweight MLOps: Deep Learning, Implementation, and Advanced Optimization
3.1 Deep learning fundamentals for edge security.
3.2 Design and implementation of optimized deep learning models.
3.3 Adversarial techniques for model evaluation and defense.
3.4 Optimization of deep learning models for the edge.
3.5 Implementation of MLOps systems for deep learning.
3.6 Use of federated learning techniques for privacy and security.
3.7 Data quality analysis and its impact on security.
3.8 Implementation of AI-based intrusion detection systems.
3.9 Risk assessment and mitigation strategies for complex systems.
3.10 Final project: Development and implementation of a secure deep learning system.
4. Master’s Degree in AI Security at the Edge and Lightweight MLOps: Comprehensive Design, Development, and Optimization
4.1 Design of comprehensive AI security architectures for the edge.
4.2 Development of MLOps systems with automation and orchestration.
4.3 Optimization of AI models for different hardware architectures.
4.4 Implementation of protection techniques against model attacks.
4.5 Integration of security throughout the model lifecycle.
4.6 Identity and Access Management (IAM) in edge environments.
4.7 Auditing and regulatory compliance for AI systems.
4.8 Design of systems resilient to failures and attacks.
4.9 Scalability strategies and resource management.
4.10 Final project: design, development, and optimization of a secure and scalable AI system.
5. Excellence in AI Security at the Edge and Lightweight MLOps: Architecture, Deployment, and Scalability
5.1 Advanced architecture of secure AI systems at the edge.
5.2 Deployment of AI models in large-scale production environments.
5.3 Horizontal and vertical scalability techniques for MLOps.
5.4 Automation of model deployment and management.
5.5 Implementation of self-healing and recovery mechanisms.
5.6 Design of real-time monitoring and alerting systems.
5.7 Integration with cloud platforms and hybrid environments.
5.8 Configuration management and version control at scale.
5.9 Cost and performance optimization strategies.
5.10 Final project: Design and implementation of a high-performance, scalable AI system.
6. Specialization in Edge AI Security and Lightweight MLOps: Development, Implementation, and Continuous Improvement
6.1 Development of customized edge AI security solutions.
6.2 Implementation of MLOps tailored to specific business needs.
6.3 Integration of security tools and frameworks.
6.4 Implementation of continuous improvement processes.
6.5 Proactive monitoring and threat detection.
6.6 Risk analysis and vulnerability assessments.
6.7 Incident response and recovery.
6.8 Stakeholder communication.
6.9 Compliance and regulation.
6.10 Final project: creation and implementation of a secure AI solution.
7. Expert Architecture and Application of Edge AI Security and Lightweight MLOps
7.1 Design of advanced security architectures for edge AI.
7.2 Implementation of customized security components. 7.3 Integration of complex and modular MLOps systems.
7.4 Implementation of security policies and controls.
7.5 Development of adaptive security solutions.
7.6 Design of incident response processes.
7.7 Selection and configuration of security tools.
7.8 Development of security tests and assessments.
7.9 Integration with security platforms.
7.10 Final project: Design and deployment of a secure and advanced AI solution.
8. Advanced Analysis and Application of AI Security at the Edge and Lightweight MLOps
8.1 Comprehensive analysis of security vulnerabilities in AI models.
8.2 Application of advanced protection techniques.
8.3 Analysis and design of high-performance MLOps systems.
8.4 Implementation of monitoring and response systems.
8.5 Analysis of trends and threats in the field of AI.
8.6 Development of mitigation strategies.
8.7 Creation of security reports and documentation.
8.8 Collaboration with security teams.
8.9 Adaptation to regulations and standards.
8.10 Final project: analysis, design, and implementation of a robust AI security solution.
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
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