Azure AI Engineer Associate (AI‑102)

Azure AI Engineer Associate (AI‑102)

Módulos

Módulo 1: Introducción a Azure AI y Arquitectura General

  • Fundamentos de inteligencia artificial en la nube.

  • Servicios cognitivos en Azure.

  • Modelos preentrenados vs. personalizados.

  • Planificación de soluciones AI a escala.

  • Uso de Computer Vision API: OCR, análisis de imágenes y videos.

  • Custom Vision para modelos personalizados.

  • Lectura de texto en tiempo real.

  • Casos de uso: seguridad, manufactura, salud.

  • Azure Language Service: análisis de sentimientos, detección de idioma.

  • Reconocimiento de entidades y frases clave.

  • Traducción automática.

  • Creación de flujos NLP personalizados con LUIS.

  • Introducción al Azure Bot Framework.

  • Integración con servicios cognitivos.

  • Diseño de conversaciones y flujos.

  • Publicación de bots en canales (Teams, Web, etc.).

  • Azure Form Recognizer y Document Intelligence.

  • Indexación con Azure Cognitive Search.

  • Pipelines de búsqueda cognitiva.

  • Integración con soluciones empresariales.

  • Fundamentos de modelos GPT y DALL·E.

  • Creación de prompts efectivos.

  • Aplicaciones prácticas de IA generativa en negocios.

  • Consideraciones éticas y de Responsible AI.

  • Uso de SDKs y APIs REST.

  • Despliegue de modelos AI con contenedores.

  • Automatización con Azure DevOps o GitHub Actions.

  • Control de versiones, escalabilidad y supervisión.

  • Configuración de roles, claves y permisos en servicios cognitivos.

  • Supervisión, auditoría y control de uso.

  • Implementación de principios de IA responsable.

  • Reporte de métricas y métricas de rendimiento.

  • Revisión general de objetivos del examen.

  • Simulacros de prueba con feedback.

  • Tips prácticos y casos reales.

  • Recomendaciones para presentar con éxito.

Technical certification that validates your ability to design, develop, implement, and maintain artificial intelligence solutions in Azure. It covers services such as computer vision, natural language processing, knowledge mining, intelligent agents, and generative models.

Upon completion, the student will be able to:

  • Design and implement AI architectures in Azure aligned with functional requirements
  • Use vision services (e
  • g
  • , Custom Vision) for image and video analysis
  • Develop NLP solutions: text analysis, entity recognition, translation, and conversational bots
  • Build knowledge mining pipelines with Azure Search and Document Intelligence
  • Implement generative solutions using Azure OpenAI (GPT models, DALL·E)
  • Integrate AI models into applications using APIs, SDKs, CI/CD, and containers
  • Manage and secure cognitive services by applying best practices in monitoring, governance, and Responsible AI

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

  • Experience in programming (Python or C#)
  • Knowledge of using REST APIs and Azure AI SDKs
  • Familiarity with the Azure portal and creating cognitive services

Azure AI Engineer Associate (AI‑102) Applies
Azure AI Engineer Associate (AI‑102) 40 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

Technical certification that validates your ability to design, develop, implement, and maintain artificial intelligence solutions in Azure. It covers services such as computer vision, natural language processing, knowledge mining, intelligent agents, and generative models.

Objectives

Upon completion, the student will be able to:

  • Design and implement AI architectures in Azure aligned with functional requirements
  • Use vision services (e
  • g
  • , Custom Vision) for image and video analysis
  • Develop NLP solutions: text analysis, entity recognition, translation, and conversational bots
  • Build knowledge mining pipelines with Azure Search and Document Intelligence
  • Implement generative solutions using Azure OpenAI (GPT models, DALL·E)
  • Integrate AI models into applications using APIs, SDKs, CI/CD, and containers
  • Manage and secure cognitive services by applying best practices in monitoring, governance, and Responsible AI

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

  • Experience in programming (Python or C#)
  • Knowledge of using REST APIs and Azure AI SDKs
  • Familiarity with the Azure portal and creating cognitive services

offers

Azure AI Engineer Associate (AI‑102) Applies
Azure AI Engineer Associate (AI‑102) 40 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.

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