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:
- Ensure that the candidate has both the theoretical knowledge and practical experience necessary to create robust and operational ML solutions on AWS
| AWS Certified Machine Learning – Specialty (MLS‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning – Specialty (MLS‑C01) | None 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
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.
Objectives
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:
- Ensure that the candidate has both the theoretical knowledge and practical experience necessary to create robust and operational ML solutions on AWS
offers
| AWS Certified Machine Learning – Specialty (MLS‑C01) | Applies |
|---|---|
| AWS Certified Machine Learning – Specialty (MLS‑C01) | None 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 Engineering
Creation of repositories such as S3, EBS, EFS, implementation of ingestion pipelines (batch/streaming) with Kinesis, Glue, EMR; data transformation through ETL, Spark, and AWS Batch
Module 2: Exploratory Data Analysis
Data cleaning and preparation, outlier detection, imputation, scaling, feature generation, and exploratory visualization using notebooks
Module 3: Feature Engineering and Visualization
Advanced techniques such as encoding, normalization, dimensionality reduction, and binning, for optimizing model performance
Module 4: Modeling – 1
Selection of the appropriate algorithm (regression, classification, clustering), configuration of initial parameters, detection of overfitting/underfitting
Module 5: Modeling – 2
Training in SageMaker, hyperparameter tuning, regularization techniques, cross-validation, and evaluation with metrics such as ROC, F1, RMSE
Module 6: Deep Learning and EML
Introduction to neural networks, frameworks such as TensorFlow / PyTorch, embedded models and recommenders, deep network structures.
Module 7: Implementation and Operations – 1
Implementation of inference endpoints (realtime & batch), appropriate hardware (CPU vs GPU), containers in SageMaker, ECS, Lambda, Terraform/CloudFormation
Module 8: Implementation and Operations – 2
CI/CD Pipelines for ML with SageMaker Pipelines, CodePipeline/Build/Deploy, automatic scaling, and phased deployment
Module 9: Monitoring and Maintenance
Monitoring endpoints with SageMaker Model Monitor, metrics/instrumentation with CloudWatch, X-Ray, logs insights
Module 10: Security and Costs
Best security practices in ML (IAM, encryption, VPC), tagging, cost optimization CloudWatch, CloudTrail
Module 11: Comprehensive Labs + Preparation
End-to-end simulation with real dataset: ingestion, EDA, training, deployment, monitoring; resolution of cases and MLS-C01 related simulations