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AWS Certified AI Practitioner (AIF‑C01)

AWS Certified AI Practitioner (AIF‑C01)

Módulos

Módulo I: Fundamentos de IA y ML

Introducción a conceptos clave como IA, ML, deep learning, redes neuronales y NLP. Estudio de tipos de inferencia (batch vs real‑time), clasificación de datos (estructurados, no estructurados), y diferencias entre aprendizaje supervisado, no supervisado y por refuerzo

Cobertura de modelos generativos: tokens, embeddings, prompt engineering. Exploración de uso en generación de texto, imágenes, audio, resumido, chatbots y código. Ciclo de vida de un modelo (pre‑entrenamiento a despliegue)

Diseño de soluciones usando foundation models: criterios de selección (costo, latencia, idioma), uso de RAG, vector databases (OpenSearch, Aurora, Neptune), técnicas de fine‑tuning, chain‑of‑thought prompting y evaluación con métricas (BLEU, ROUGE, BERTScore)

Implementación de IA ética: equidad, inclusión, transparencia y explicabilidad. Uso de herramientas como SageMaker Clarify, Model Cards, guardrails en Bedrock. Cobertura de sesgos, impacto ambiental e implicaciones legales

Gestión de seguridad en soluciones AI: IAM, cifrado, Macie, PrivateLink; modelo de responsabilidad compartida. Estrategias de gobernanza, auditoría, cumplimiento (ISO, SOC), AWS Config e Inspector

Revisión práctica de herramientas AWS relevantes: SageMaker, Bedrock, Comprehend, Rekognition, Lex, Polly, Translate; infraestructuras subyacentes (S3, EC2, Lambda, VPC)

Desarrollo hands‑on: pipelines de ML simples, Q&A sobre prompt tuning, uso de Bedrock y evaluación de modelos con SageMaker tools.

Simulados con formato oficial (multiple‑choice, matching, ordering, case study), flashcards y revisión de patrones de preguntas

Análisis de escenarios reales en finanzas, salud, marketing, soporte: selección de modelos, flujo de datos, riesgos y mitigación.

Integración de IA en procesos empresariales: seguridad en flujos AI, monitoreo, eficiencia de costos, escalabilidad.

Resumen de mejores prácticas, recursos (Well‑Architected, whitepapers, exam guide), plan de estudio personalizado y checklist previo al examen.

This course provides you with a clear and structured understanding of the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), using Amazon Web Services (AWS) tools and services. Designed for professionals with no prior programming or data science experience, the program prepares you to obtain the official AWS Certified AI Practitioner (AIF-C01) certification.

Throughout the course, you will learn to identify real-world AI use cases, distinguish between different types of learning (supervised, unsupervised, and reinforcement learning), and understand how generative language models such as Foundation Models work. You will also explore key AWS services such as SageMaker JumpStart, Amazon Bedrock, Amazon Q, and PartyRock, applying governance and ethical principles to the responsible use of AI.

Upon completing this certification, the student will be able to:

  • Understand essential concepts of AI, ML, and generative models
  • Identify and assess the most common AI use cases in business environments
  • Apply pretrained models and AI services available on AWS
  • Implement best practices for the ethical and responsible use of artificial intelligence
  • Recognize key aspects of security, privacy, and governance in AI projects

No prior knowledge is strictly required, but it is highly recommended that the candidate:

  • Has a general understanding of cloud computing
  • Understands basic data and programming concepts
  • Is familiar with the AWS environment (console, IAM, basic services)
  • Purpose of these prerequisites:
  • To ensure a smooth learning curve focused on the practical application of AI models, without getting bogged down in overly technical fundamentals

AWS Certified AI Practitioner (AIF‑C01) Applies
AWS Certified AI Practitioner (AIF‑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 now

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Description

This course provides you with a clear and structured understanding of the fundamental concepts of Artificial Intelligence (AI) and Machine Learning (ML), using Amazon Web Services (AWS) tools and services. Designed for professionals with no prior programming or data science experience, the program prepares you to obtain the official AWS Certified AI Practitioner (AIF-C01) certification.

Throughout the course, you will learn to identify real-world AI use cases, distinguish between different types of learning (supervised, unsupervised, and reinforcement learning), and understand how generative language models such as Foundation Models work. You will also explore key AWS services such as SageMaker JumpStart, Amazon Bedrock, Amazon Q, and PartyRock, applying governance and ethical principles to the responsible use of AI.

Objectives

Upon completing this certification, the student will be able to:

  • Understand essential concepts of AI, ML, and generative models
  • Identify and assess the most common AI use cases in business environments
  • Apply pretrained models and AI services available on AWS
  • Implement best practices for the ethical and responsible use of artificial intelligence
  • Recognize key aspects of security, privacy, and governance in AI projects

No prior knowledge is strictly required, but it is highly recommended that the candidate:

  • Has a general understanding of cloud computing
  • Understands basic data and programming concepts
  • Is familiar with the AWS environment (console, IAM, basic services)
  • Purpose of these prerequisites:
  • To ensure a smooth learning curve focused on the practical application of AI models, without getting bogged down in overly technical fundamentals

offers

AWS Certified AI Practitioner (AIF‑C01) Applies
AWS Certified AI Practitioner (AIF‑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.

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 now
Download Syllabus
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