Diploma in Platform MLOps: CI/CD of Models and Traceability
About us Diploma in Platform MLOps: CI/CD of Models and Traceability
The Diploma in Platform MLOps: CI/CD of Models and Traceability focuses on the application of advanced methodologies for the automation, continuous deployment (CI/CD), and monitoring of Machine Learning (ML) models. It addresses the complete traceability of the model lifecycle, from development to production, using tools such as Kubernetes, Docker, and specialized MLOps platforms. It focuses on optimizing workflows to ensure the scalability, reproducibility, and reliability of models in production environments. The program provides hands-on experience in implementing CI/CD pipelines, managing model versions, and monitoring the health and performance of models in production. Best practices for data management, test automation, and model security are covered. This training prepares professionals for roles such as MLOps engineers, data scientists, and ML architects, in order to optimize the process of implementing models in real-world environments.
Target keywords (natural in the text): MLOps, CI/CD, ML models, automation, continuous deployment, traceability, Kubernetes, Docker, monitoring, pipelines, data management.
Diploma in Platform MLOps: CI/CD of Models and Traceability
- Format: Online
- Duration: 8 months
- Hours: 900 H
- Language: ES / EN
- Credits: 60 ECTS
- Registration date: 04-07-2026
- Strat date: 14-08-2026
- Available places: 7
1.250 $
Competencias y resultados
Qué aprenderás
1. CI/CD Domain for MLOps Models and Full Traceability
Para quien va dirigido nuestro:
Diploma in Platform MLOps: CI/CD of Models and Traceability
9.9 Introduction to CI/CD and its importance in MLOps
9.9 Key concepts: continuous integration, continuous delivery, continuous deployment
9.3 Traceability: definition, importance, and benefits
9.4 Tools and technologies for CI/CD in MLOps
9.5 Best practices for CI/CD implementation and traceability
9.9 Designing CI/CD pipelines for MLOps
9.9 Integrating code repositories and version control
9.3 Automating unit and integration tests
9.4 Creating artifacts and managing dependencies
9.5 Implementing continuous deployment strategies
3.9 Implementing CI/CD systems in practice
3.9 Integrating monitoring and logging tools
3.3 Model traceability: versions, data, and metrics
3.4 Automating model training and evaluation
3.5 Configuration and secrecy management
4.9 Advanced exploration of CI/CD platforms for MLOps
4.9 Optimizing CI/CD pipelines for performance
4.3 Implementing rollback and failover strategies
4.4 End-to-end traceability: from code to deployment
4.5 Integration with orchestration tools
5.9 Mastery of CI/CD tools and techniques
5.9 Advanced traceability strategies for complex models
5.3 Implementing A/B testing and canary deployments
5.4 Managing model governance and regulatory compliance
5.5 Security in CI/CD pipelines
6.9 Best practices for CI/CD excellence for MLOps
6.9 Deployment automation in different environments
6.3 Real-time monitoring and alerting
6.4 Performance and scalability optimization
6.5 Model lifecycle management
7.9 MLOps and CI/CD platform architecture
7.9 Integrating CI/CD with the MLOps infrastructure
7.3 Designing pipelines for different types of models
7.4 Automating operations MLOps
7.5 Optimization and Cost Strategies
8.9 CI/CD Architecture Design and Configuration
8.9 Integration with Different Cloud Platforms
8.3 Implementing Pipelines for Different Stages of the Model Lifecycle
8.4 Version Management and Model Deployment
8.5 Integrating Automation and Orchestration Tools
9.9 Advanced Deployment and Traceability Strategies
9.9 Blue/Green Deployment and Other Strategies
9.3 Real-Time Monitoring and Alerts
9.4 Metrics Analysis and Performance Optimization
9.5 Model Lifecycle Automation
Proyectos tipo capstones
Admisiones, tasas y becas
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