Course Modules
Módulo 1: Fundamentos de Machine Learning e IA en AWS
-
Ecosistema de servicios de ML e IA en la nube
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Arquitectura base de soluciones de ML
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Uso de servicios de IA listos para usar (Amazon Rekognition, Comprehend, etc.)
Módulo 2: Ingesta y Almacenamiento de Datos
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Opciones de almacenamiento de datos (Amazon S3, EFS, FSx)
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Ingesta de datos en streaming (Amazon Kinesis, Apache Kafka)
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Formatos de datos y mecanismos de extracción
Módulo 3: Transformación e Ingeniería de Características
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Limpieza y procesamiento de datos (AWS Glue, AWS Glue DataBrew)
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Ingeniería de características con Amazon SageMaker Data Wrangler
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Almacenamiento y gestión con SageMaker Feature Store
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Integridad de datos y mitigación de sesgos (SageMaker Clarify)
Módulo 4: Diseño y Selección de Modelos ML
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Algoritmos integrados de Amazon SageMaker
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Modelos fundacionales y plantillas (Amazon Bedrock, SageMaker JumpStart)
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Evaluación de viabilidad y selección de enfoques
Módulo 5: Entrenamiento, Ajuste y Evaluación de Modelos
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Entornos de entrenamiento y frameworks soportados (TensorFlow, PyTorch)
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Optimización y ajuste de hiperparámetros (AMT)
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Técnicas de regularización y prevención de sobreajuste
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Métricas de rendimiento y depuración del modelo (SageMaker Model Debugger)
Módulo 6: Despliegue de Modelos y Gestión de Infraestructura
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Opciones de inferencia y tipos de endpoints (Tiempo real, Serverless, Batch)
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Contenedores para ML (Amazon ECR, BYOC en SageMaker)
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Infraestructura como Código (AWS CloudFormation, AWS CDK) y Auto Scaling
Módulo 7: MLOps: Orquestación y Automatización (CI/CD)
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Principios de CI/CD aplicados a Machine Learning
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Orquestación de flujos de trabajo con Amazon SageMaker Pipelines
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Integración de repositorios y automatización (CodePipeline, CodeBuild, EventBridge)
Módulo 8: Monitorización, Seguridad y Optimización Continua
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Monitorización de inferencia y detección de desviación o drift (SageMaker Model Monitor)
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Optimización de rendimiento y costos de infraestructura (Spot Instances, Cost Explorer)
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Gobernanza y seguridad (Roles IAM, políticas, aislamiento de VPC y cifrado)
Current process description
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) course prepares professionals to build, operationalize, deploy, and maintain scalable and secure machine learning (ML) solutions and pipelines on AWS. Under the Practical Learning Method approach, participants receive a cloud entrance pack to apply concepts in real environments through hands-on workshops and labs, learning to ingest and transform data, train and refine models, automate orchestration with CI/CD pipelines, and secure ML workflows. Upon completion, you will master key operational solutions such as model and endpoint deployment (Amazon SageMaker), deep monitoring of inference performance (SageMaker Clarify and Model Monitor), and infrastructure optimization, leaving you fully prepared to perform effectively as a Machine Learning Engineer and successfully pass the new international certification exam.
Objectives
At the end of the course, participants will be able to:
- Ingest, transform, and prepare data for modeling, ensuring data integrity and mitigating biases using tools like AWS Glue and Amazon SageMaker Data Wrangler
- Select, train, and refine ML models, choosing the appropriate algorithms, optimizing hyperparameters, and evaluating performance metrics
- Automate provisioning and workflows by setting up continuous integration and continuous delivery (CI/CD) pipelines using SageMaker Pipelines and AWS orchestration tools
- Monitor and optimize inference in production environments, detecting data drift and anomalies using Amazon SageMaker Model Monitor and SageMaker Clarify
- Implement advanced security and compliance for ML workloads in AWS, including access controls (IAM), data anonymization, and secure network isolation (VPC)
- Manage and optimize ML infrastructure, selecting the appropriate compute and storage resources for training and inference while controlling costs
- Prepare for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification, ensuring the mastery of the best cloud ML engineering practices
Prerequisites
Preferred schedule
To participate in this training, attendees must meet the following requirements:
- Holding the AWS Certified Cloud Practitioner certification (or equivalent training) or demonstrating at least 1 year of hands-on experience using Amazon SageMaker and other AWS services for ML engineering, plus experience in related roles (Data Engineer, DevOps, Software Developer, or Data Scientist)
- Solid knowledge of common Machine Learning algorithms, data engineering fundamentals (data formats, ingestion, and transformation), alongside a basic understanding of CI/CD pipelines and Infrastructure as Code (IaC)
- Familiarity with software engineering best practices, data querying tools, version control repositories (e
- g
- , Git), and proficiency in at least one programming language used for data and ML (ideally Python)
What Does This Course Include?
| Our Value Proposition | Benefit for Participant or Company |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) International Certification | 40 hours |
| • E-learning reinforcement topics and exclusive materials and simulators | • Complementary platform with digital resources, study guides, support recordings and exam simulators. |
| • Flexible educational model (in-person or live remote) | • Possibility to choose modality without losing human interaction and teaching support. |
| • Focus on employability and professional performance and exam preparation. | • Preparation oriented to certification, job performance and professional scaling based on practice, enhanced with AI support. |
| • Integration of Artificial Intelligence in learning | • Students use AI tools to reinforce understanding, practice exams and enhance their productivity. |
| • International Certification included | • Official endorsement with global recognition. Includes certification exams and access to the partner platform. |
| • Laboratories in real learning environments. | • Unlimited practical experience with real lab accounts and access to professional cloud infrastructure. |
| • Live classes with certified expert instructors. | • Guided and personalized training with direct real-time support, not offline. Recorded classes only for review. |
| • Personalized attention, small groups. | • Individual tracking, progress evaluations and technical support during training. AI-proctored performance examiner. |
| • Post-certification support and extended access to resources | • Post-assistance, access to materials for and continuous updates. |
| • Practical methodology and real and/or simulated projects. | • Applied learning from day one: simulations, business cases, projects and real cloud environments. |
| • Certificates of Approval and/or participation. | • International Certification by AWS ACADEMY MEMBER INSTITUTION
• Infinity Training Institute USA: International Certification Diploma in English • Aula Matriz IETDH Colombia - Certificate of participation |
Why choose Infinity Training Institute?
All our courses are Internationally certified:
- Infinity Training Institute USA: International Certification Diploma in English.
- International Certification from our partners Microsoft, Oracle, Certitalents, AWS, PMI, Cisco, etc.
2. We are not an automatic platform, nor self-study through videos
We are a unique model, in-person or Remote modality (with live instructor). Technology + pedagogy + AI + expert instructors + real practice — not self-study or outdated content.
3. Training designed for today's job market
Experience applicable immediately in interviews, technical tests and real work environments.
4. Real or simulated projects with international standards
Students access real environments such as Azure, AWS, Google Cloud, Oracle Cloud, develop real or simulated projects, building a demonstrable technical portfolio depending on the type of course or certification they have chosen.
5. A unique combination in Latin America and the USA
Certification + real practice + AI + continuous support + dual diploma.
6. Proven results
More than 95% of our graduates obtain official certification and improve professionally in less than six months.
7. Competency level guarantee
If the student during the practical training process does not reach a minimum performance level of 75%, they must repeat the course if the instructor determines so, and the student has shown the commitment and minimum class attendance required by the model.
Your professional future starts here
At Infinity Training Institute we boost your growth with an innovative, flexible model focused on real learning and performance evaluation. Certified instructors, intensive practice, integrated AI and constant support: world-class training.
Learning Methodology
At Infinity Training Institute, we apply a comprehensive, ever-evolving methodology centered on practical learning, powered by Artificial Intelligence, enabling personalized instruction, performance assessment, and optimized preparation for international certifications with certified instructors, real labs, simulators, and e-learning platforms. Participants learn by doing, developing technical and professional skills in small groups, with personalized follow-up and pre and post-certification support. Infinity Training Institute: Learn. Apply. Get Certified. Transcend.
Current process description
The AWS Certified Machine Learning Engineer - Associate (MLA-C01) course prepares professionals to build, operationalize, deploy, and maintain scalable and secure machine learning (ML) solutions and pipelines on AWS. Under the Practical Learning Method approach, participants receive a cloud entrance pack to apply concepts in real environments through hands-on workshops and labs, learning to ingest and transform data, train and refine models, automate orchestration with CI/CD pipelines, and secure ML workflows. Upon completion, you will master key operational solutions such as model and endpoint deployment (Amazon SageMaker), deep monitoring of inference performance (SageMaker Clarify and Model Monitor), and infrastructure optimization, leaving you fully prepared to perform effectively as a Machine Learning Engineer and successfully pass the new international certification exam.
Objectives
At the end of the course, participants will be able to:
- Ingest, transform, and prepare data for modeling, ensuring data integrity and mitigating biases using tools like AWS Glue and Amazon SageMaker Data Wrangler
- Select, train, and refine ML models, choosing the appropriate algorithms, optimizing hyperparameters, and evaluating performance metrics
- Automate provisioning and workflows by setting up continuous integration and continuous delivery (CI/CD) pipelines using SageMaker Pipelines and AWS orchestration tools
- Monitor and optimize inference in production environments, detecting data drift and anomalies using Amazon SageMaker Model Monitor and SageMaker Clarify
- Implement advanced security and compliance for ML workloads in AWS, including access controls (IAM), data anonymization, and secure network isolation (VPC)
- Manage and optimize ML infrastructure, selecting the appropriate compute and storage resources for training and inference while controlling costs
- Prepare for the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification, ensuring the mastery of the best cloud ML engineering practices
Prerequisites
Preferred schedule
To participate in this training, attendees must meet the following requirements:
- Holding the AWS Certified Cloud Practitioner certification (or equivalent training) or demonstrating at least 1 year of hands-on experience using Amazon SageMaker and other AWS services for ML engineering, plus experience in related roles (Data Engineer, DevOps, Software Developer, or Data Scientist)
- Solid knowledge of common Machine Learning algorithms, data engineering fundamentals (data formats, ingestion, and transformation), alongside a basic understanding of CI/CD pipelines and Infrastructure as Code (IaC)
- Familiarity with software engineering best practices, data querying tools, version control repositories (e
- g
- , Git), and proficiency in at least one programming language used for data and ML (ideally Python)
What Does This Course Include?
| Our Value Proposition | Benefit for Participant or Company |
|---|---|
| AWS Certified Machine Learning Engineer – Associate (MLA‑C01) International Certification | 40 hours |
| • E-learning reinforcement topics and exclusive materials and simulators | • Complementary platform with digital resources, study guides, support recordings and exam simulators. |
| • Flexible educational model (in-person or live remote) | • Possibility to choose modality without losing human interaction and teaching support. |
| • Focus on employability and professional performance and exam preparation. | • Preparation oriented to certification, job performance and professional scaling based on practice, enhanced with AI support. |
| • Integration of Artificial Intelligence in learning | • Students use AI tools to reinforce understanding, practice exams and enhance their productivity. |
| • International Certification included | • Official endorsement with global recognition. Includes certification exams and access to the partner platform. |
| • Laboratories in real learning environments. | • Unlimited practical experience with real lab accounts and access to professional cloud infrastructure. |
| • Live classes with certified expert instructors. | • Guided and personalized training with direct real-time support, not offline. Recorded classes only for review. |
| • Personalized attention, small groups. | • Individual tracking, progress evaluations and technical support during training. AI-proctored performance examiner. |
| • Post-certification support and extended access to resources | • Post-assistance, access to materials for and continuous updates. |
| • Practical methodology and real and/or simulated projects. | • Applied learning from day one: simulations, business cases, projects and real cloud environments. |
| • Certificates of Approval and/or participation. | • International Certification by AWS ACADEMY MEMBER INSTITUTION
• Infinity Training Institute USA: International Certification Diploma in English • Aula Matriz IETDH Colombia - Certificate of participation |
Why choose Infinity Training Institute?
1. Internationally guaranteed certificationsAll our courses are Internationally certified:
- Infinity Training Institute USA: International Certification Diploma in English.
- International Certification from our partners Microsoft, Oracle, Certitalents, AWS, PMI, Cisco, etc.
2. We are not an automatic platform, nor self-study through videos
We are a unique model, in-person or Remote modality (with live instructor). Technology + pedagogy + AI + expert instructors + real practice — not self-study or outdated content.
3. Training designed for today's job market
Experience applicable immediately in interviews, technical tests and real work environments.
4. Real or simulated projects with international standards
Students access real environments such as Azure, AWS, Google Cloud, Oracle Cloud, develop real or simulated projects, building a demonstrable technical portfolio depending on the type of course or certification they have chosen.
5. A unique combination in Latin America and the USA
Certification + real practice + AI + continuous support + dual diploma.
6. Proven results
More than 95% of our graduates obtain official certification and improve professionally in less than six months.
7. Competency level guarantee
If the student during the practical training process does not reach a minimum performance level of 75%, they must repeat the course if the instructor determines so, and the student has shown the commitment and minimum class attendance required by the model.
Your professional future starts here
At Infinity Training Institute we boost your growth with an innovative, flexible model focused on real learning and performance evaluation. Certified instructors, intensive practice, integrated AI and constant support: world-class training.
Learning Methodology
At Infinity Training Institute, we apply a comprehensive, ever-evolving methodology centered on practical learning, powered by Artificial Intelligence, enabling personalized instruction, performance assessment, and optimized preparation for international certifications with certified instructors, real labs, simulators, and e-learning platforms. Participants learn by doing, developing technical and professional skills in small groups, with personalized follow-up and pre and post-certification support. Infinity Training Institute: Learn. Apply. Get Certified. Transcend.