Creación de repositorios como S3, EBS, EFS, implementación de pipelines de ingesta (batch/streaming) con Kinesis, Glue, EMR; transformación de datos mediante ETL, Spark y AWS Batch
Limpieza y preparación de datos, detección de outliers, imputación, escalado, generación de features y visualización exploratoria usando notebooks
Técnicas avanzadas como codificación, normalización, reducción dimensional, binning, para optimización del rendimiento de modelos
Selección del algoritmo adecuado (regresión, clasificación, clustering), configuración de parámetros iniciales, detección de overfitting/underfitting
Entrenamiento en SageMaker, ajuste de hiperparámetros, técnicas de regularización, validación cruzada y evaluación con métricas como ROC, F1, RMSE
Incursión en redes neuronales, frameworks como TensorFlow / PyTorch, modelos embebidos y recomendadores, estructuras de redes profundas .
Implementación de endpoints de inferencia (realtime & batch), hardware adecuado (CPU vs GPU), contenedores en SageMaker, ECS, Lambda, Terraform/CloudFormation
Pipelines CI/CD para ML con SageMaker Pipelines, CodePipeline/Build/Deploy, escalado automático y despliegue por fases
Monitoreo de endpoints con SageMaker Model Monitor, métricas/instrumentación con CloudWatch, X‑Ray, logs insights
Mejores prácticas de seguridad en ML (IAM, cifrado, VPC), etiquetado, cost optimization CloudWatch, CloudTrail
Simulación end-to-end con dataset real: ingesta, EDA, training, deployment, monitoreo; resolución de casos y simulacros MLS‑C01 relacionados
Advanced certification designed for professionals with at least two years of experience developing, architecting, and deploying Machine Learning (ML) or Deep Learning solutions on AWS. It validates the ability to design, build, train, optimize, and maintain ML models that solve business problems in the cloud.
Design and build scalable data pipelines for ML on AWS. Effectively clean, explore, and visualize datasets. Select and train appropriate models for business problems. Optimize models using tuning techniques and identify overfitting. Deploy models to production using services such as SageMaker. Monitor performance and costs, and manage model versions. Implement security and compliance practices for ML models.
At least 1–2 years of practical experience deploying ML or Deep Learning solutions. 🔷 Understand and explain basic ML algorithms. 🔷 Experience in hyperparameter optimization. 🔷 Familiarity with ML/DL frameworks (TensorFlow, PyTorch, MXNet). 🔷 Know best practices for model training, deployment, and monitoring. Primary objective:
| AWS Certified Machine Learning – Specialty (MLS‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning – Specialty (MLS‑C01) | None hours |
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor (live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
The methodology seeks that the student does not memorize, but rather understands the concepts and how they are applied in a work environment.
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.
Pre-enroll nowMake your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
Advanced certification designed for professionals with at least two years of experience developing, architecting, and deploying Machine Learning (ML) or Deep Learning solutions on AWS. It validates the ability to design, build, train, optimize, and maintain ML models that solve business problems in the cloud.
Design and build scalable data pipelines for ML on AWS. Effectively clean, explore, and visualize datasets. Select and train appropriate models for business problems. Optimize models using tuning techniques and identify overfitting. Deploy models to production using services such as SageMaker. Monitor performance and costs, and manage model versions. Implement security and compliance practices for ML models.
At least 1–2 years of practical experience deploying ML or Deep Learning solutions. 🔷 Understand and explain basic ML algorithms. 🔷 Experience in hyperparameter optimization. 🔷 Familiarity with ML/DL frameworks (TensorFlow, PyTorch, MXNet). 🔷 Know best practices for model training, deployment, and monitoring. Primary objective:
| AWS Certified Machine Learning – Specialty (MLS‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning – Specialty (MLS‑C01) | None hours |
The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.
The instructor(live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.
La metodología persigue que el estudiante "does not memorize", but rather "understands" the concepts and how they are applied in a work environment."
As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.
You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.
Make your payment quickly, safely and reliably
- For bank transfer payments, request the details by email
capacita@aulamatriz.edu.co.
- If you wish to finance your payment through our credit options
(Sufi, Cooperativa Unimos or Fincomercio), click on the following link:
Ver opciones de crédito.
