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:
- Design and run ML pipelines on AWS
- Train, evaluate, and tune models using SageMaker
- Automate deployments with CI/CD and infrastructure as code
- Monitor models in production and respond to drift or errors
- Ensure compliance with security and access policies
To participate in this training, attendees must meet the following requirements:
- Minimum 1 year of experience in ML Engineering on AWS (SageMaker, Glue, etc
- )
- Previous recommended roles: Data Scientist, Data Engineer, Backend, or DevOps
- Knowledge of basic ML, coding, CI/CD, deployment, and monitoring
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | 24 hours |
Learning Methodology
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.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Pre-enrollment
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 nowInfinity Payments
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.
Description
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.
Objectives
Upon completion of the training, you will be able to:
- Design and run ML pipelines on AWS
- Train, evaluate, and tune models using SageMaker
- Automate deployments with CI/CD and infrastructure as code
- Monitor models in production and respond to drift or errors
- Ensure compliance with security and access policies
To participate in this training, attendees must meet the following requirements:
- Minimum 1 year of experience in ML Engineering on AWS (SageMaker, Glue, etc
- )
- Previous recommended roles: Data Scientist, Data Engineer, Backend, or DevOps
- Knowledge of basic ML, coding, CI/CD, deployment, and monitoring
offers
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) | 24 hours |
Learning Methodology
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.
Conditions to guarantee successful results:
- a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
- b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
- c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.
Infinity Payments
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.
Course Modules
Module 1: Data Preparation and Cleaning
-
Data ingestion and storage in S3, EFS, FSx, EBS, RDS, DynamoDB
-
Batch and streaming processing (Kinesis, Data Wrangler, Glue)
-
Cleaning techniques, transformation, and feature engineering
-
Outlier detection, handling missing data, masking/anonymization
-
Bias prevention in data and preparation for training
Module 2: Development of ML Models
-
Algorithm selection, pre-built models, and AI services (Rekognition, Translate, Bedrock)
-
Training with SageMaker: hyperparameters, regularization, overfitting/underfitting
-
Version management in SageMaker Model Registry
-
Evaluation metrics: confusion matrix, ROC, F1, RMSE; interpretation with SageMaker Clarify
-
Debugging models with SageMaker Model Debugger
Module 3: Deployment and Orchestration of Models
-
Types of inference endpoints: real-time, batch, serverless
-
Selection and provisioning of instances (CPU vs GPU) and containers (SageMaker, ECR, ECS/EKS, Lambda)
-
Infrastructure engineering as code: CloudFormation, CDK
-
Auto-scaling strategies based on operational metrics
-
CI/CD for ML: pipelines with CodePipeline, CodeBuild, CodeDeploy, SageMaker Pipelines
Module 4: Monitoring, Maintenance, and Security
-
Inference and data monitoring: SageMaker Model Monitor, drift detection
-
Infrastructure monitoring: CloudWatch, X-Ray, Logs Insights, Lambda Insights
-
Cost management: CloudTrail, Cost Explorer, and resource tagging
-
Security and access: IAM, roles, policies, VPC, SageMaker Security features
-
Best practices for secure CI/CD and audits
Module 5: Comprehensive Practical Labs
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Execution of a complete pipeline: from data ingestion to deployment and monitoring in production
-
Multi-stage implementations using real or simulated infrastructure
-
Use of real and challenging datasets in business scenarios
-
Workshops on data debugging, training, and endpoints
Module 6: Exam Simulations and Preparation
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Simulations with official styles: multiple choice, multiple answers, ordering, matching
-
Case studies by domain, discussion of solutions
-
Review of relevant whitepapers and the AWS Well-Architected Framework
-
Study strategies based on identified gaps and Q&A sessions