Azure AI Engineer Associate (AI‑102)

Azure AI Engineer Associate Certification (AI-102)

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.

40 hours
Official Certificate
Expert Instructors
Online Learning
Certificación internacional Azure AI Engineer Associate (AI‑102)
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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

Certificación internacional Azure AI Engineer Associate (AI‑102) Applies
Certificación internacional 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

Certificación internacional Azure AI Engineer Associate (AI‑102) Applies
Certificación internacional 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.

Course Modules

Module 1: Introduction to Azure AI and General Architecture

  • Fundamentals of cloud artificial intelligence.

  • Cognitive services in Azure.

  • Pre-trained vs. custom models.

  • Planning AI solutions at scale.

  • Use of Computer Vision API: OCR, image and video analysis.

  • Custom Vision for custom models.

  • Real-time text reading.

  • Use cases: security, manufacturing, healthcare.

  • Azure Language Service: sentiment analysis, language detection.

  • Entity recognition and key phrase extraction.

  • Machine translation.

  • Creating custom NLP flows with LUIS.

  • Introduction to the Azure Bot Framework.

  • Integration with cognitive services.

  • Designing conversations and flows.

  • Publishing bots on channels (Teams, Web, etc.).

  • Azure Form Recognizer and Document Intelligence.

  • Indexing with Azure Cognitive Search.

  • Cognitive search pipelines.

  • Integration with enterprise solutions.

  • Fundamentals of GPT and DALL·E models.

  • Creating effective prompts.

  • Practical applications of generative AI in business.

  • Ethical considerations and Responsible AI.

  • Use of SDKs and REST APIs.

  • Deployment of AI models with containers.

  • Automation with Azure DevOps or GitHub Actions.

  • Version control, scalability, and monitoring.

  • Configuration of roles, keys, and permissions in cognitive services.

  • Monitoring, auditing, and usage control.

  • Implementation of responsible AI principles.

  • Reporting metrics and performance metrics.

  • General review of exam objectives.

  • Practice tests with feedback.

  • Practical tips and real cases.

  • Recommendations for successful presentation.