Ingesta y almacenamiento de datos en S3, EFS, FSx, EBS, RDS, DynamoDB
Procesamiento por lotes y streaming (Kinesis, Data Wrangler, Glue)
Técnicas de limpieza, transformación y ingeniería de características
Detección de valores atípicos, manejo de datos faltantes, enmascaramiento/anónimos
Prevención de sesgos en datos y preparación para entrenamiento
Selección de algoritmos, modelos preconstruidos y servicios AI (Rekognition, Translate, Bedrock)
Entrenamiento con SageMaker: hiperparámetros, regularización, over/underfitting
Manejo de versiones en SageMaker Model Registry
Métricas de evaluación: matriz de confusión, ROC, F1, RMSE; interpretación con SageMaker Clarify
Depuración de modelos con SageMaker Model Debugger
Tipos de endpoints de inferencia: tiempo real, batch, serverless
Selección y aprovisionamiento de instancias (CPU vs GPU) y contenedores (SageMaker, ECR, ECS/EKS, Lambda)
Ingeniería de infraestructuras como código: CloudFormation, CDK
Estrategias de escalado automático basado en métricas operacionales
CI/CD para ML: pipelines con CodePipeline, CodeBuild, CodeDeploy, SageMaker Pipelines
Monitoreo de inferencia y datos: SageMaker Model Monitor, detección de drifts
Monitorización de infraestructura: CloudWatch, X-Ray, Logs Insights, Lambda Insights
Gestión de costos: CloudTrail, Cost Explorer y etiquetado de recursos
Seguridad y acceso: IAM, roles, políticas, VPC, SageMaker Security features
Buenas prácticas para CI/CD seguros y auditorías
Ejecución de pipeline completo: desde ingesta de datos hasta despliegue y monitoreo en producción
Implementaciones multietapa usando infraestructura real o simulada
Uso de datasets reales y retadores en escenarios empresariales
Talleres de debugging de datos, entrenamiento y endpoints
Simulados con estilos oficiales: opción múltiple, múltiples respuestas, ordenar, matching
Casos de estudio por dominio, discusión de soluciones
Revisión de whitepapers y AWS Well-Architected Framework relevantes
Estrategias de estudio basado en gaps identificados y sesiones de Q&A
Associate-level certification focused on validating your skills in implementing, operating, and maintaining machine learning solutions in production using AWS, specifically Amazon SageMaker. It validates practical skills in pipeline creation, CI/CD, security, and MLOps.
Upon completion of the training, you will be able to:
To participate in this training, attendees must meet the following requirements:
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | 24 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.
Associate-level certification focused on validating your skills in implementing, operating, and maintaining machine learning solutions in production using AWS, specifically Amazon SageMaker. It validates practical skills in pipeline creation, CI/CD, security, and MLOps.
Upon completion of the training, you will be able to:
To participate in this training, attendees must meet the following requirements:
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | 24 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.
