MLOps course applied to electric fleets
About our
MLops Course Applied to Electric Fleets
This course focuses on the implementation of Machine Learning (ML) and DevOps to optimize the management of electric vehicle fleets. It covers the design of robust data pipelines, the development of predictive models for maintenance, load management, and route optimization, using tools such as Kubernetes, Docker, and cloud computing platforms. It emphasizes the automation of the ML lifecycle, from training to deployment and continuous monitoring, improving efficiency and reducing operating costs.
This course provides hands-on experience in creating monitoring dashboards, implementing alerting systems, and managing model versioning. Participants will learn how to scale ML models in production environments, improve uptime, and ensure data security. This training prepares professionals for roles such as MLOps engineers, data scientists, fleet analysts, and software developers, driving innovation in the electric mobility sector.
MLOps course applied to electric fleets
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
599 $
Competencies and results
What you will learn
1. MLOps Optimization for Performance and Intelligent Management of Electric Fleets
- Develop MLOps strategies for real-time monitoring and analysis of electric fleet data.
- Implement predictive models to optimize the performance of batteries and electrical components.
- Manage the lifecycle of machine learning models, from development to deployment and continuous updates.
- Automate model training, evaluation, and deployment processes for efficient fleet management.
- Utilize MLOps tools and platforms specifically designed for electric fleets, such as battery health monitoring.
- Optimize the infrastructure and computational resources for model training and inference.
- Analyze telemetry data to identify behavioral patterns and optimize energy consumption.
- Apply machine learning techniques to predict failures and perform preventive maintenance.
- Design dashboards and visualizations for fleet performance monitoring and data-driven decision-making.
Integrating machine learning models into fleet management systems for route optimization and load planning.
2. Implementation of Advanced MLOps: Predictive Modeling and Optimization of Electric Fleets
Here is the requested content:
- Build and manage MLOps pipelines for predictive modeling of electric fleets.
- Develop advanced predictive models to optimize the performance and efficiency of electric fleets.
- Apply machine learning (ML) techniques to predict failures, optimize energy consumption, and plan maintenance.
- Implement data-driven fleet optimization strategies, including load management and resource allocation.
- Use MLOps tools and platforms for automation, monitoring, and lifecycle management of models.
- Analyze and evaluate the impact of decisions based on predictive models on the operation of electric fleets.
- Integrate predictive models with existing fleet management systems.
- Learn how to scale and maintain machine learning models in production environments.
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. MLOps: Efficiency Prediction, Predictive Maintenance and Data Management in Electric Fleets
4. MLOps: Efficiency Prediction, Predictive Maintenance, and Data Management in Electric Fleets
Development of Machine Learning models to predict the energy efficiency of electric fleets, considering factors such as speed, load, and environmental conditions.
Implementation of predictive maintenance systems through sensor data analysis, with the aim of anticipating failures in key components and minimizing downtime.
Management and analysis of large volumes of data (Big Data) from various sources (sensors, management systems, etc.) to optimize the performance and reliability of electric fleets.
Optimization of battery charging and discharging schedules, using optimization algorithms and considering energy tariffs and resource availability.
Real-time monitoring of the status of batteries and electric propulsion systems to detect anomalies and prevent risky situations.
5. Strategic MLOps: Predictive Analysis, Route Optimization and Scalability for Electric Fleets
- Develop predictive models for optimizing electric vehicle fleet routes, considering variables such as energy consumption, weather conditions, and charging station availability.
- Apply data analysis and machine learning techniques to predict failures in electric vehicle fleet components and schedule preventive maintenance, reducing downtime.
- Implement strategies for optimizing the charging and unloading of electric vehicles, maximizing efficiency and minimizing operating costs.
- Design and implement scalability solutions for electric vehicle fleets, considering fleet growth, charging infrastructure, and data processing capacity.
- Use MLOps tools to automate the lifecycle of predictive models, including training, validation, deployment, and continuous monitoring.
- Integrate diverse data sources, such as vehicle sensor data, weather data, and charging station data, to improve the accuracy of predictive models.
- Analyze the impact of decisions made based on predictive models on the operational efficiency and profitability of electric fleets.
- Develop interactive dashboards and visualizations to monitor the performance of electric fleets and facilitate data-driven decision-making.
- Apply data security and privacy principles to protect sensitive information from electric fleets.
- Explore the use of emerging technologies, such as edge computing and blockchain, to improve the efficiency and security of electric fleets.
6. Implementation of MLOps for Energy Optimization and Predictive Maintenance in Electric Fleets
6. Implementation of MLOps for Energy Optimization and Predictive Maintenance in Electric Fleets
- MLOPs Fundamentals: Key concepts, methodologies, and best practices for implementing MLOps in electric fleets.
- Data Collection and Preprocessing: Techniques for collecting data from sensors and fleet management systems, cleaning, transforming, and preparing data for analysis.
- Energy Optimization Modeling: Development and implementation of machine learning models to optimize the energy consumption of electric fleets, including demand forecasting, load management, and route optimization.
- Predictive Maintenance Modeling: Creation of models to predict failures in electric fleet components (batteries, motors, etc.), using machine learning techniques such as time series analysis, classification, and anomaly detection.
- Model Deployment and Monitoring: Strategies for deploying ML models in Production environments, performance monitoring, retraining, and model lifecycle management.
Workflow Automation: Automating data pipelines and ML using workflow orchestration and management tools.
Tools and Technologies: Using MLOps platforms and tools, such as Kubeflow, MLflow, TensorFlow Extended (TFX), and other open-source and commercial solutions.
Case Study: Practical application of MLOps in a real-world energy optimization and predictive maintenance project for an electric fleet.
Regulatory and Security Aspects: Considerations regarding data privacy, security, and regulatory compliance in MLOps implementation.
Future Trends: Exploring emerging trends in MLOps and their impact on electric fleet management.
Who this program is for:
MLOps course applied to electric fleets
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields with a focus on electric mobility.
- Specialists and technicians from OEM companies specializing in rotary-wing/eVTOL (electric vertical takeoff and landing) aircraft, MRO (maintenance, repair, and overhaul), aerospace consulting, or employees of technology centers with fleet electrification projects.
- Professionals in areas such as Flight Testing, aeronautical certification, avionics, flight control, and flight dynamics who wish to deepen their knowledge and skills in The application of MLOps.
Officials, regulators, and experts from aviation authorities and professionals involved in UAM/eVTOL (urban air mobility) projects who need to acquire skills in compliance and data management in the context of electric fleets.
Recommended requirements: A solid understanding of aerodynamic principles, automatic control, and structural analysis is highly beneficial. A B2+ or C1 level of English (ES/EN) is required for optimal learning. We offer bridging tracks for those who need to strengthen their prior knowledge.
- 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.
1.1 Fundamentals of MLOps and its Application in Electric Fleets
1.2 Challenges and Opportunities in Electric Fleet Management
1.3 Introduction to the MLOps Lifecycle
1.4 Data Collection and Preparation for Electric Fleets
1.5 Data Exploration and Analysis in the Naval Context
1.6 Introduction to Predictive Fleet Modeling
1.7 Key Metrics and Model Evaluation
1.8 MLOps Tools and Platforms
1.9 Introduction to Model Monitoring and Deployment
1.10 Case Studies: Applications of MLOps in Electric Fleets
2.2 Introduction to Predictive Modeling in Electric Fleets
2.2 Fundamentals of MLOps for Fleet Optimization
2.3 Design and Development of Predictive Models
2.4 Implementation of MLOps for Predictive Maintenance
2.5 Route Optimization and Energy Efficiency
2.6 Real-Time Data Monitoring and Analysis
2.7 Scalability Strategies in Electric Fleets
2.8 Model Lifecycle Automation
2.9 Tools and Technologies for Advanced MLOps
2.20 Case Studies: Practical Applications of MLOps
3.3 Introduction to Efficiency and Scalability in Electric Fleets
3.2 MLOps Optimization Strategies for Performance
3.3 Predictive Modeling and Data Analysis for Energy Efficiency
3.4 Predictive Maintenance and Reduction of Operating Costs
3.5 Scalability of MLOps Solutions in Electric Fleets
3.6 Route Design and Optimization with MLOps
3.7 Intelligent Data Management and MLOps Platforms
3.8 Risk Assessment and Strategic Decision Making
3.9 Implementing MLOps for Sustainability
3.30 Case Studies: Success Stories and Lessons Learned in MLOps Implementation
4.4 Introduction to MLOps in Electric Fleets: Fundamentals and Benefits
4.2 Data Collection and Preparation for Predictive Modeling
4.3 Predictive Modeling for Fleet Energy Efficiency
4.4 Predictive Maintenance: Strategies and Failure Models
4.5 Implementing MLOps for Route Optimization
4.6 Data and Pipeline Management in Electric Fleets
4.7 Monitoring and Evaluating the Performance of MLOps Models
4.8 Scalability and Deployment of MLOps Solutions
4.9 Case Studies: Real-World Applications of MLOps in Electric Fleets
4.40 Future Trends and Advancements in MLOps for Electric Fleets
5.5 Predictive Demand Analysis and Strategic Fleet Planning
5.5 Route Optimization and Intelligent Scheduling
5.3 Energy Efficiency and Consumption Prediction
5.4 Predictive Maintenance and Asset Lifecycle Management
5.5 Scalability and Data Management for Electric Fleets
5.6 Real-Time Monitoring and Performance Analysis
5.7 Integration of MLOps Models with Fleet Management Systems
5.8 Risk Mitigation Strategies and Data-Driven Decision Making
5.9 Cost-Benefit Analysis (ROI) in MLOps Implementation
5.50 Case Studies: Practical Applications and Best Practices
6.6 Introduction to MLOps in Electric Fleets: Fundamentals and Key Concepts
6.2 Data Collection and Preparation for MLOps in Electric Fleets
6.3 Predictive Modeling for Fleet Energy Optimization
6.4 Predictive Maintenance with MLOps: Fault Detection and Scheduling
6.5 Route and Performance Optimization with MLOps in Electric Fleets
6.6 Data Management and MLOps Pipelines for Electric Fleets
6.7 Deployment and Monitoring of MLOps Models in Electric Fleet Environments
6.8 Scalability and Sustainability of MLOps in Electric Fleets
6.9 Case Studies: Practical Applications of MLOps in Electric Fleets
6.60 Future Trends and Challenges of MLOps in the Electric Marine Sector
- 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.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).